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93 results on '"ISI Foundation Institute for Scientific Interchange"'

1. Simplicially driven simple contagion

Author

Maxime Lucas, Iacopo Iacopini, Thomas Robiglio, Alain Barrat, Giovanni Petri, ISI Foundation Institute for Scientific Interchange, Central European University Vienna, Dipartimento di Fisica [Torino], Università degli studi di Torino = University of Turin (UNITO), CPT - E5 Physique statistique et systèmes complexes, Centre de Physique Théorique - UMR 7332 (CPT), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), and ANR-19-CE46-0008,DataRedux,Réduction de données massives pour la simulation numérique prédictive(2019)

Subjects
Social and Information Networks (cs.SI), FOS: Computer and information sciences, Physics - Physics and Society, [PHYS.PHYS.PHYS-SOC-PH]Physics [physics]/Physics [physics]/Physics and Society [physics.soc-ph], FOS: Physical sciences, General Physics and Astronomy, Computer Science - Social and Information Networks, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Physics and Society (physics.soc-ph), [PHYS.PHYS.PHYS-DATA-AN]Physics [physics]/Physics [physics]/Data Analysis, Statistics and Probability [physics.data-an]
Abstract

Single contagion processes are known to display a continuous transition from an epidemic-free phase at low contagion rates to the epidemic state for rates above a critical threshold. This transition can become discontinuous when two simple contagion processes are coupled in a bi-directional symmetric way. However, in many cases, the coupling is not symmetric and the processes can be of a different nature. For example, risky social behaviors -- such as not wearing masks or engaging in large gatherings -- can affect the spread of a disease, and their adoption dynamics via social reinforcement mechanisms are better described by complex contagion models, rather than by the simple contagion paradigm, which is more appropriate for disease spreading phenomena. Motivated by this example, we consider a simplicial contagion (describing the adoption of a behavior) that uni-directionally drives a simple contagion (describing a disease propagation). We show that, above a critical driving strength, such driven simple contagion can exhibit both discontinuous transitions and bi-stability, which are instead absent in standard simple contagions. We provide a mean-field analytical description of the phase diagram of the system, and complement the results with Markov-chain simulations. Our results provide a novel route for a simple contagion process to display the phenomenology of a higher-order contagion, through a driving mechanism that may be hidden or unobservable in many practical instances., Comment: 10 pages, 7 figures. I.I. and M.L. contributed equally to this work. A.B and G.P. jointly supervised this work

Published
2023

2. Influential groups for seeding and sustaining nonlinear contagion in heterogeneous hypergraphs

Author

Guillaume St-Onge, Iacopo Iacopini, Vito Latora, Alain Barrat, Giovanni Petri, Antoine Allard, Laurent Hébert-Dufresne, Université Laval [Québec] (ULaval), Centre de Physique Théorique - UMR 7332 (CPT), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), CPT - E5 Physique statistique et systèmes complexes, Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Queen Mary University of London (QMUL), ISI Foundation Institute for Scientific Interchange, and ANR-19-CE46-0008,DataRedux,Réduction de données massives pour la simulation numérique prédictive(2019)

Subjects
Physics - Physics and Society, Physics, QC1-999, Complex networks, FOS: Physical sciences, General Physics and Astronomy, Physics and Society (physics.soc-ph), Computer Science::Social and Information Networks, Nonlinear phenomena, Astrophysics, 01 natural sciences, 010305 fluids & plasmas, QB460-466, Phase transitions and critical phenomena, 0103 physical sciences, [PHYS.COND.CM-SM]Physics [physics]/Condensed Matter [cond-mat]/Statistical Mechanics [cond-mat.stat-mech], 010306 general physics
Abstract

Several biological and social contagion phenomena, such as superspreading events or social reinforcement, are the results of multi-body interactions, for which hypergraphs offer a natural mathematical description. In this paper, we develop a novel mathematical framework based on approximate master equations to study contagions on random hypergraphs with a heterogeneous structure, both in terms of group size (hyperedge cardinality) and of membership of nodes to groups (hyperdegree). The characterization of the inner dynamics of groups provides an accurate description of the contagion process, without losing the analytical tractability. Using a contagion model where multi-body interactions are mapped onto a nonlinear infection rate, our two main results show how large groups are influential, in the sense that they drive both the early spread of a contagion and its endemic state (i.e., its stationary state). First, we provide a detailed characterization of the phase transition, which can be continuous or discontinuous with a bistable regime, and derive analytical expressions for the critical and tricritical points. We find that large values of the third moment of the membership distribution suppress the emergence of a discontinuous phase transition. Furthermore, the combination of heterogeneous group sizes and nonlinear contagion facilitates the onset of a mesoscopic localization phase, where contagion is sustained only by the largest groups, thereby inhibiting bistability as well. Second, we formulate the problem of optimal seeding for hypergraph contagion, and we compare two strategies: tuning the allocation of seeds according to either individual node or group properties. We find that, when the contagion is sufficiently nonlinear, groups are more effective seeds of contagion than individual nodes., 23 pages, 12 figures (main text); 6 pages, 3 figures (supplementary information)

Published
2022

3. Are People Optimistically Biased about the Risk of COVID-19 Infection? Lessons from the First Wave of the Pandemic in Europe

Author

Kathleen McColl, Marion Debin, Cecile Souty, Caroline Guerrisi, Clement Turbelin, Alessandra Falchi, Isabelle Bonmarin, Daniela Paolotti, Chinelo Obi, Jim Duggan, Yamir Moreno, Ania Wisniak, Antoine Flahault, Thierry Blanchon, Vittoria Colizza, Jocelyn Raude, École des Hautes Études en Santé Publique [EHESP] (EHESP), Unité des Virus Emergents (UVE), Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Pierre Louis d'Epidémiologie et de Santé Publique (iPLESP), Sorbonne Université (SU)-Institut National de la Santé et de la Recherche Médicale (INSERM), EA Bioscope Corse Méditerranée : Dynamique des infections virales en milieu insulaire, Université Pascal Paoli (UPP)-Institut National de la Santé et de la Recherche Médicale (INSERM), Santé publique France - French National Public Health Agency [Saint-Maurice, France], Data Science Laboratory (ISI), ISI Foundation Institute for Scientific Interchange, Public Health England [London], National University of Ireland [Galway] (NUI Galway), University of Zaragoza - Universidad de Zaragoza [Zaragoza], University of Geneva [Switzerland], Département des sciences humaines et sociales (SHS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU), and Université de Genève = University of Geneva (UNIGE)

Subjects
risk perception, optimism bias, unrealistic optimism, COVID-19, pandemic, Europe, Optimism, SARS-CoV-2, Health, Toxicology and Mutagenesis, Public Health, Environmental and Occupational Health, [SHS.PSY]Humanities and Social Sciences/Psychology, Article, Humans, Medicine, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Pandemics
Abstract

International audience; Unrealistic optimism, the underestimation of one’s risk of experiencing harm, has been investigated extensively to understand better and predict behavioural responses to health threats. Prior to the COVID-19 pandemic, a relative dearth of research existed in this domain regarding epidemics, which is surprising considering that this optimistic bias has been associated with a lack of engagement in protective behaviours critical in fighting twenty-first-century, emergent, infectious diseases. The current study addresses this gap in the literature by investigating whether people demonstrated optimism bias during the first wave of the COVID-19 pandemic in Europe, how this changed over time, and whether unrealistic optimism was negatively associated with protective measures. Taking advantage of a pre-existing international participative influenza surveillance network (n = 12,378), absolute and comparative unrealistic optimism were measured at three epidemic stages (pre-, early, peak), and across four countries—France, Italy, Switzerland and the United Kingdom. Despite differences in culture and health response, similar patterns were observed across all four countries. The prevalence of unrealistic optimism appears to be influenced by the particular epidemic context. Paradoxically, whereas absolute unrealistic optimism decreased over time, comparative unrealistic optimism increased, suggesting that whilst people became increasingly accurate in assessing their personal risk, they nonetheless overestimated that for others. Comparative unrealistic optimism was negatively associated with the adoption of protective behaviours, which is worrying, given that these preventive measures are critical in tackling the spread and health burden of COVID-19. It is hoped these findings will inspire further research into sociocognitive mechanisms involved in risk appraisal.

Published
2022

4. Mechanisms for lyssavirus persistence in non-synanthropic bats in Europe: insights from a modeling study

Author

Raphaëlle Métras, Vittoria Colizza, Andrea Apolloni, Jordi Serra-Cobo, Marc López-Roig, Hervé Bourhy, Chiara Poletto, Davide Colombi, ISI Foundation Institute for Scientific Interchange, University of Turin, Universitat de Barcelona (UB), Animal, Santé, Territoires, Risques et Ecosystèmes (UMR ASTRE), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA), Université de Montpellier (UM), Institut Sénégalais de Recherches Agricoles [Dakar] (ISRA), Institut Pierre Louis d'Epidémiologie et de Santé Publique (iPLESP), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU), Centre National de Référence de la Rage-Dynamique des Lyssavirus et adaptation à l'hôte (CNR), Institut Pasteur [Paris], Universitat de Barcelona, European Project: 278433,EC:FP7:HEALTH,FP7-HEALTH-2011-two-stage,PREDEMICS(2011), Università degli studi di Torino = University of Turin (UNITO), and Institut Pasteur [Paris] (IP)

Subjects
0301 basic medicine, Virologia, lcsh:Medicine, Myotis myotis, Adaptive Immunity, L73 - Maladies des animaux, Persistence (computer science), Sexual Behavior, Animal, 0302 clinical medicine, Theoretical, Models, Chiroptera, Zoonoses, Bats, Artificial induction of immunity, lcsh:Science, Multidisciplinary, biology, Caves, Habitat, [SDV.MP.VIR]Life Sciences [q-bio]/Microbiology and Parasitology/Virology, Veterinary medicine and animal Health, Public Health, Seasons, Autre (Sciences du Vivant), Sexual Behavior, Zoology, Metapopulation, Article, Ratapinyades, 03 medical and health sciences, Animal Migration, Animals, Ecosystem, Humans, Lyssavirus, Models, Theoretical, Rhabdoviridae Infections, Spain, Virology, medicine, [SDV.BA.MVSA]Life Sciences [q-bio]/Animal biology/Veterinary medicine and animal Health, Animal, Host (biology), lcsh:R, medicine.disease, biology.organism_classification, 030104 developmental biology, Médecine vétérinaire et santé animal, Rabies, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, lcsh:Q, 030217 neurology & neurosurgery
Abstract

Bats are natural reservoirs of the largest proportion of viral zoonoses among mammals, thus understanding the conditions for pathogen persistence in bats is essential to reduce human risk. Focusing on the European Bat Lyssavirus subtype 1 (EBLV-1), causing rabies disease, we develop a data-driven spatially explicit metapopulation model to investigate EBLV-1 persistence in Myotis myotis and Miniopterus schreibersii bat species in Catalonia. We find that persistence relies on host spatial structure through the migratory nature of M. schreibersii, on cross-species mixing with M. myotis, and on survival of infected animals followed by temporary immunity. The virus would not persist in the single colony of M. myotis. Our study provides for the first time epidemiological estimates for EBLV-1 progression in M. schreibersii. Our approach can be readily adapted to other zoonoses of public health concern where long-range migration and habitat sharing may play an important role.

Published
2019

5. Relevance of temporal cores for epidemic spread in temporal networks

Author

Francesco Gullo, Martino Ciaperoni, Alain Barrat, Francesco Bonchi, Ciro Cattuto, Edoardo Galimberti, Yahoo! Research [Barcelona], Data Science Laboratory (ISI), ISI Foundation Institute for Scientific Interchange, CPT - E5 Physique statistique et systèmes complexes, Centre de Physique Théorique - UMR 7332 (CPT), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), ANR-19-CE46-0008,DataRedux,Réduction de données massives pour la simulation numérique prédictive(2019), Department of Computer Science, Institute for Scientific Interchange Foundation, UniCredit, Aalto-yliopisto, and Aalto University

Subjects
0301 basic medicine, Physics - Physics and Society, Theoretical computer science, Computer science, Process (engineering), Complex networks, FOS: Physical sciences, lcsh:Medicine, Physics and Society (physics.soc-ph), 01 natural sciences, Article, 03 medical and health sciences, Group cohesiveness, 0103 physical sciences, Relevance (information retrieval), [PHYS.COND.CM-SM]Physics [physics]/Condensed Matter [cond-mat]/Statistical Mechanics [cond-mat.stat-mech], Duration (project management), lcsh:Science, Quantitative Biology - Populations and Evolution, 010306 general physics, Multidisciplinary, lcsh:R, Populations and Evolution (q-bio.PE), Graph, Variety (cybernetics), Identification (information), 030104 developmental biology, FOS: Biological sciences, lcsh:Q, Statistical physics, Centrality
Abstract

Temporal networks are widely used to represent a vast diversity of systems, including in particular social interactions, and the spreading processes unfolding on top of them. The identification of structures playing important roles in such processes remains largely an open question, despite recent progresses in the case of static networks. Here, we consider as candidate structures the recently introduced concept of span-cores: the span-cores decompose a temporal network into subgraphs of controlled duration and increasing connectivity, generalizing the core-decomposition of static graphs. To assess the relevance of such structures, we explore the effectiveness of strategies aimed either at containing or maximizing the impact of a spread, based respectively on removing span-cores of high cohesiveness or duration to decrease the epidemic risk, or on seeding the process from such structures. The effectiveness of such strategies is assessed in a variety of empirical data sets and compared to baselines that use only static information on the centrality of nodes and static concepts of coreness, as well as to a baseline based on a temporal centrality measure. Our results show that the most stable and cohesive temporal cores play indeed an important role in epidemic processes on temporal networks, and that their nodes are likely to include influential spreaders.

Published
2020

6. Long-range movements coupled with heterogeneous incubation period sustain dog rabies at the national scale in Africa

Author

Colombi, Davide, Poletto, Chiara, Nakouné, Emmanuel, Bourhy, Hervé, Colizza, Vittoria, Institut Pierre Louis d'Epidémiologie et de Santé Publique (iPLESP), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU), ISI Foundation Institute for Scientific Interchange, University of Turin, Institut Pasteur de Bangui, Réseau International des Instituts Pasteur (RIIP), Centre collaborateur de l'OMS - Rage / World Health Organization Collaboration Centres - Rabies (CC-OMS / WHO-CC), Institut Pasteur [Paris]-Organisation Mondiale de la Santé / World Health Organization Office (OMS / WHO), This work has been partially supported by the EC-Health contract no. 278433 (PREDEMICS), contract no. 517727 (RABMEDCONTROL)., Bodescot, Myriam, Università degli studi di Torino = University of Turin (UNITO), Institut Pasteur [Paris] (IP)-Organisation Mondiale de la Santé / World Health Organization Office (OMS / WHO), and Sorbonne Université (SU)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
Rural Population, RNA viruses, Viral Diseases, Human mobility, Urban Population, RC955-962, Social Sciences, MESH: Central African Republic / epidemiology, Pathology and Laboratory Medicine, Geographical locations, Infectious Disease Incubation Period, MESH: Dogs, MESH: Rural Population, [SDV.MHEP.MI]Life Sciences [q-bio]/Human health and pathology/Infectious diseases, Arctic medicine. Tropical medicine, Zoonoses, Medicine and Health Sciences, MESH: Animals, Dog Diseases, MESH: Travel, [SDV.MP.VIR] Life Sciences [q-bio]/Microbiology and Parasitology/Virology, Mammals, Travel, Geography, Eukaryota, Viral Persistence and Latency, Central African Republic, Domestic animals, MESH: Urban Population, Infectious Diseases, Medical Microbiology, Viral Pathogens, Vertebrates, Viruses, [SDV.MP.VIR]Life Sciences [q-bio]/Microbiology and Parasitology/Virology, [SDV.MHEP.MI] Life Sciences [q-bio]/Human health and pathology/Infectious diseases, MESH: Dog Diseases / epidemiology, MESH: Infectious Disease Incubation Period, Public aspects of medicine, RA1-1270, Pathogens, MESH: Zoonoses, MESH: Rabies / veterinary, Research Article, Neglected Tropical Diseases, Rabies, Animal Types, Human Geography, Models, Biological, Microbiology, Dogs, Virology, Animals, Humans, Cities, Microbial Pathogens, Pets and companion animals, MESH: Humans, [SDV.BA.MVSA]Life Sciences [q-bio]/Animal biology/Veterinary medicine and animal Health, [SDV.BA.MVSA] Life Sciences [q-bio]/Animal biology/Veterinary medicine and animal Health, Organisms, MESH: Models, Biological, Biology and Life Sciences, Tropical Diseases, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, Rabies virus, MESH: Animal Distribution, Amniotes, Africa, Earth Sciences, Lyssavirus, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, People and places, Animal Distribution, Zoology
Abstract

Dog-transmitted rabies is responsible for more than 98% of human cases worldwide, remaining a persistent problem in developing countries. Mass vaccination targets predominantly major cities, often compromising disease control due to re-introductions. Previous work suggested that areas neighboring cities may behave as the source of these re-introductions. To evaluate this hypothesis, we introduce a spatially explicit metapopulation model for rabies diffusion in Central African Republic. Calibrated on epidemiological data for the capital city, Bangui, the model predicts that long-range movements are essential for continuous re-introductions of rabies-exposed dogs across settlements, eased by the large fluctuations of the incubation period. Bangui’s neighborhood, instead, would not be enough to self-sustain the epidemic, contrary to previous expectations. Our findings suggest that restricting long-range travels may be very efficient in limiting rabies persistence in a large and fragmented dog population. Our framework can be applied to other geographical contexts where dog rabies is endemic., Author summary Rabid dogs constitute the main source of rabies transmission to humans, therefore understanding disease dynamics in dogs is essential to reduce human risk. Mathematical models are invaluable in exploring intervention scenarios where field experiments can be expensive or impractical. We developed a data-driven model to identify the mechanisms for rabies dispersal, accounting for dogs’ spatial fragmentation, human-mediated movements, and empirically estimated fluctuations of the incubation period in dogs. Applied to Central African Republic, our model indicates that continuous re-introductions of rabid dogs via human-mediated movements are critical in sustaining the disease in the country. Long-range travel restrictions can be very effective in aiding rabies control. Our approach can be extended to other geographical settings.

Published
2020

7. weg2vec: Event embedding for temporal networks

Author

Laetitia Gauvin, Maddalena Torricelli, Márton Karsai, Torricelli M., Karsai M., Gauvin L., Data Science Laboratory (ISI), ISI Foundation Institute for Scientific Interchange, Dynamic Networks : Temporal and Structural Capture Approach (DANTE), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire de l'Informatique du Parallélisme (LIP), Centre National de la Recherche Scientifique (CNRS)-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École normale supérieure - Lyon (ENS Lyon)-Centre National de la Recherche Scientifique (CNRS)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École normale supérieure - Lyon (ENS Lyon)-Institut Rhône-Alpin des systèmes complexes (IXXI), École normale supérieure - Lyon (ENS Lyon)-Université Lumière - Lyon 2 (UL2)-Université Jean Moulin - Lyon 3 (UJML), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Université Lumière - Lyon 2 (UL2)-Université Jean Moulin - Lyon 3 (UJML), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut Rhône-Alpin des systèmes complexes (IXXI), and École normale supérieure de Lyon (ENS de Lyon)-Université Lumière - Lyon 2 (UL2)-Université Jean Moulin - Lyon 3 (UJML)

Subjects
0301 basic medicine, FOS: Computer and information sciences, Physics - Physics and Society, Computer Science - Machine Learning, Theoretical computer science, Computer science, Complex networks, FOS: Physical sciences, lcsh:Medicine, 02 engineering and technology, Physics and Society (physics.soc-ph), Article, [INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI], Machine Learning (cs.LG), 03 medical and health sciences, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Code (cryptography), Projection (set theory), Representation (mathematics), lcsh:Science, Structure (mathematical logic), Social and Information Networks (cs.SI), Multidisciplinary, Event (computing), Complex networks Computer Science Temporal Network Embedding, lcsh:R, Computer Science - Social and Information Networks, Outcome (probability), 030104 developmental biology, Embedding, lcsh:Q
Abstract

Network embedding techniques are powerful to capture structural regularities in networks and to identify similarities between their local fabrics. However, conventional network embedding models are developed for static structures, commonly consider nodes only and they are seriously challenged when the network is varying in time. Temporal networks may provide an advantage in the description of real systems, but they code more complex information, which could be effectively represented only by a handful of methods so far. Here, we propose a new method of event embedding of temporal networks, called weg2vec, which builds on temporal and structural similarities of events to learn a low dimensional representation of a temporal network. This projection successfully captures latent structures and similarities between events involving different nodes at different times and provides ways to predict the final outcome of spreading processes unfolding on the temporal structure.

Published
2020

8. DyANE: Dynamics-aware node embedding for temporal networks

Author

Sato, Koya, Oka, Mizuki, Barrat, Alain, Cattuto, Ciro, CPT - E5 Physique statistique et systèmes complexes, Centre de Physique Théorique - UMR 7332 (CPT), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Data Science Laboratory (ISI), and ISI Foundation Institute for Scientific Interchange

Subjects
Social and Information Networks (cs.SI), FOS: Computer and information sciences, Physics - Physics and Society, Computer Science - Machine Learning, [PHYS.PHYS.PHYS-SOC-PH]Physics [physics]/Physics [physics]/Physics and Society [physics.soc-ph], FOS: Physical sciences, Computer Science - Social and Information Networks, Physics and Society (physics.soc-ph), Machine Learning (cs.LG), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

Low-dimensional vector representations of network nodes have proven successful to feed graph data to machine learning algorithms and to improve performance across diverse tasks. Most of the embedding techniques, however, have been developed with the goal of achieving dense, low-dimensional encoding of network structure and patterns. Here, we present a node embedding technique aimed at providing low-dimensional feature vectors that are informative of dynamical processes occurring over temporal networks -- rather than of the network structure itself -- with the goal of enabling prediction tasks related to the evolution and outcome of these processes. We achieve this by using a modified supra-adjacency representation of temporal networks and building on standard embedding techniques for static graphs based on random-walks. We show that the resulting embedding vectors are useful for prediction tasks related to paradigmatic dynamical processes, namely epidemic spreading over empirical temporal networks. In particular, we illustrate the performance of our approach for the prediction of nodes' epidemic states in a single instance of a spreading process. We show how framing this task as a supervised multi-label classification task on the embedding vectors allows us to estimate the temporal evolution of the entire system from a partial sampling of nodes at random times, with potential impact for nowcasting infectious disease dynamics., updated version with additional results

Published
2019

9. Unsupervised extraction of epidemic syndromes from participatory influenza surveillance self-reported symptoms

Author

Ricardo Mexia, Sandro Meloni, Carl Koppeschaar, Chinelo Obi, Ana O. Franco, Kyriaki Kalimeri, Jim Duggan, Matteo Delfino, Daniela Paolotti, John Edmunds, C Kjelsø, Clément Turbelin, Daniela Perrotta, Vittoria Colizza, Ciro Cattuto, Yamir Moreno, Caroline Guerrisi, Richard Pebody, Data Science Laboratory (ISI), ISI Foundation Institute for Scientific Interchange, Institut Pierre Louis d'Epidémiologie et de Santé Publique (iPLESP), Sorbonne Université (SU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Sorbonne Université (SU), National University of Ireland [Galway] (NUI Galway), London School of Hygiene and Tropical Medicine (LSHTM), Public Health England [London], Instituto Gulbenkian de Ciência [Oeiras] (IGC), Fundação Calouste Gulbenkian, University of Zaragoza - Universidad de Zaragoza [Zaragoza], Instituto de fisic Interdisciplinar y Sistemas Complejos ( IFISC (CSIC-UIB)), Statens Serum Institut [Copenhagen], Instituto Nacional de Saùde Dr Ricardo Jorge [Portugal] (INSA), Agencia Estatal de Investigación (España), European Commission, Institute for Scientific Interchange Foundation, Fondazione Cassa di Risparmio di Torino, Gobierno de Aragón, FENOL, Ministerio de Economía y Competitividad (España), and Ministerio de Ciencia, Innovación y Universidades (España)

Subjects
0301 basic medicine, [SDV]Life Sciences [q-bio], Influenza-like illness (ILI), Disease, Seasonal influenza, 0302 clinical medicine, Epidemiology, 030212 general & internal medicine, Epidemiological Surveillance, lcsh:QH301-705.5, 0303 health sciences, Disease surveillance, education.field_of_study, Surveillance, Ecology, Incidence, Incidence (epidemiology), Syndrome, Observação em Saúde e Vigilância, 3. Good health, Europe, Geography, Computational Theory and Mathematics, Modeling and Simulation, Data Interpretation, Statistical, symbols, The Internet, Seasons, Medical emergency, influenza, Algorithms, medicine.medical_specialty, Population, MEDLINE, Cellular and Molecular Neuroscience, symbols.namesake, 03 medical and health sciences, Environmental health, Influenza, Human, Genetics, medicine, Humans, Epidemics, Probabilistic framework, education, Molecular Biology, Ecology, Evolution, Behavior and Systematics, Internet, Models, Statistical, 030306 microbiology, business.industry, Cuidados de Saúde, Computational Biology, Citizen journalism, medicine.disease, Estados de Saúde e de Doença, Pearson product-moment correlation coefficient, 030104 developmental biology, lcsh:Biology (General), [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Self Report, business, Sentinel Surveillance, 030217 neurology & neurosurgery, Unsupervised Machine Learning
Abstract

Seasonal influenza surveillance is usually carried out by sentinel general practitioners (GPs) who compile weekly reports based on the number of influenza-like illness (ILI) clinical cases observed among visited patients. This traditional practice for surveillance generally presents several issues, such as a delay of one week or more in releasing reports, population biases in the health-seeking behaviour, and the lack of a common definition of ILI case. On the other hand, the availability of novel data streams has recently led to the emergence of non-traditional approaches for disease surveillance that can alleviate these issues. In Europe, a participatory web-based surveillance system called Influenzanet represents a powerful tool for monitoring seasonal influenza epidemics thanks to aid of self-selected volunteers from the general population who monitor and report their health status through Internet-based surveys, thus allowing a real-time estimate of the level of influenza circulating in the population. In this work, we propose an unsupervised probabilistic framework that combines time series analysis of self-reported symptoms collected by the Influenzanet platforms and performs an algorithmic detection of groups of symptoms, called syndromes. The aim of this study is to show that participatory web-based surveillance systems are capable of detecting the temporal trends of influenza-like illness even without relying on a specific case definition. The methodology was applied to data collected by Influenzanet platforms over the course of six influenza seasons, from 2011-2012 to 2016-2017, with an average of 34,000 participants per season. Results show that our framework is capable of selecting temporal trends of syndromes that closely follow the ILI incidence rates reported by the traditional surveillance systems in the various countries (Pearson correlations ranging from 0.69 for Italy to 0.88 for the Netherlands, with the sole exception of Ireland with a correlation of 0.38). The proposed framework was able to forecast quite accurately the ILI trend of the forthcoming influenza season (2016-2017) based only on the available information of the previous years (2011-2016). Furthermore, to broaden the scope of our approach, we applied it both in a forecasting fashion to predict the ILI trend of the 2016-2017 influenza season (Pearson correlations ranging from 0.60 for Ireland and UK, and 0.85 for the Netherlands) and also to detect gastrointestinal syndrome in France (Pearson correlation of 0.66). The final result is a near-real-time flexible surveillance framework not constrained by any specific case definition and capable of capturing the heterogeneity in symptoms circulation during influenza epidemics in the various European countries., The authors declare no competing financial interests. D.Pa. and D.Pe. acknowledge support from H2020 FETPROACT-GSS CIMPLEX Grant No. 641191. KK, CC, D.Pa., D.Pe., Y.M. and M.D. acknowledge support from the Lagrange Project of the Institute for Scientific Interchange Foundation (ISI Foundation) funded by Fondazione Cassa di Risparmio di Torino (Fondazione CRT). Y.M. acknowledges support from the Government of Aragon, Spain through a grant to the group FENOL and by Ministry of Economy and Competitiveness (MINECO) and European Regional Development Fund (FEDER) (Grant No. FIS2017-87519-P). S.M. acknowledges support from the Spanish State Research Agency, through the María de Maeztu Program for Units of Excellence in R&D (MDM-2017-0711 to the IFISC Institute).

Published
2019

10. Randomized reference models for temporal networks

Author

Laetitia Gauvin, Mathieu Génois, Márton Karsai, Mikko Kivelä, Taro Takaguchi, Eugenio Valdano, Christian L. Vestergaard, Data Science Laboratory (ISI), ISI Foundation Institute for Scientific Interchange, CPT - E5 Physique statistique et systèmes complexes, Centre de Physique Théorique - UMR 7332 (CPT), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de l'Informatique du Parallélisme (LIP), École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Dynamic Networks : Temporal and Structural Capture Approach (DANTE), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire de l'Informatique du Parallélisme (LIP), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut Rhône-Alpin des systèmes complexes (IXXI), Université Jean Moulin - Lyon 3 (UJML), Université de Lyon-Université de Lyon-Université Lumière - Lyon 2 (UL2)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université Joseph Fourier - Grenoble 1 (UJF)-École normale supérieure - Lyon (ENS Lyon)-Université Jean Moulin - Lyon 3 (UJML), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Joseph Fourier - Grenoble 1 (UJF), Institut Rhône-Alpin des systèmes complexes (IXXI), Université de Lyon-Université Joseph Fourier - Grenoble 1 (UJF)-École normale supérieure - Lyon (ENS Lyon), Aalto University School of Science and Technology, National Institute of Information and Communications Technology (NICT), Semel Institute for Neuroscience and Human Behavior, University of California [Los Angeles] (UCLA), University of California-University of California, Décision et processus Bayesiens / Decision and Bayesian Computation, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Hub Bioinformatique et Biostatistique - Bioinformatics and Biostatistics HUB, Centre National de la Recherche Scientifique (CNRS)-Institut Pasteur [Paris], Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Central European University [Budapest, Hongrie] (CEU), Aalto University School of Science and Technology [Aalto, Finland], National Institute of Information and Communications Technology [Tokyo, Japan] (NICT), Semel Institute for Neuroscience and Human Behavior [Los Angeles, Ca], Institut Pierre Louis d'Epidémiologie et de Santé Publique (iPLESP), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU), Décision et processus Bayesiens - Decision and Bayesian Computation, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), ANR-20-CE45-0021,SiNCoBe,Base structurelle du calcul neuronal et du comportement(2020), ANR-15-CE38-0011,SoSweet,Une sociolinguistique de Twitter : liens sociaux et variations linguistiques(2015), ANR-16-CE28-0013,DyLNet,Dynamiques langagières, apprentissages linguistiques et sociabilité à l'école maternelle : apport des capteurs de proximité pour le recueil de données massives(2016), ANR-12-MONU-0018,HarMS-flu,Approches multi-échelles pour la modélisation de la propagation de la grippe pilotée par les données.(2012), European Project: 317532,EC:FP7:ICT,FP7-ICT-2011-8,MULTIPLEX(2012), École normale supérieure - Lyon (ENS Lyon)-Université Lumière - Lyon 2 (UL2)-Université Joseph Fourier - Grenoble 1 (UJF)-Université Jean Moulin - Lyon 3 (UJML), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Lumière - Lyon 2 (UL2)-Université Joseph Fourier - Grenoble 1 (UJF)-Université Jean Moulin - Lyon 3 (UJML), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Data Science Laboratory ( ISI ), Dynamic Networks : Temporal and Structural Capture Approach ( DANTE ), Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -Laboratoire de l'Informatique du Parallélisme ( LIP ), École normale supérieure - Lyon ( ENS Lyon ) -Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique ( Inria ) -Centre National de la Recherche Scientifique ( CNRS ) -École normale supérieure - Lyon ( ENS Lyon ) -Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique ( CNRS ) -Institut Rhône-Alpin des systèmes complexes ( IXXI ), Université Jean Moulin - Lyon III ( UJML ), Université de Lyon-Université de Lyon-Université Lumière - Lyon 2 ( UL2 ) -Centre National de la Recherche Scientifique ( CNRS ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National des Sciences Appliquées de Lyon ( INSA Lyon ), Université de Lyon-Institut National des Sciences Appliquées ( INSA ) -Institut National des Sciences Appliquées ( INSA ) -Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Université Joseph Fourier - Grenoble 1 ( UJF ) -École normale supérieure - Lyon ( ENS Lyon ) -Université Jean Moulin - Lyon III ( UJML ), Université de Lyon-Université Lumière - Lyon 2 ( UL2 ) -Centre National de la Recherche Scientifique ( CNRS ) -Institut National des Sciences Appliquées de Lyon ( INSA Lyon ), Université de Lyon-Institut National des Sciences Appliquées ( INSA ) -Institut National des Sciences Appliquées ( INSA ) -Université Joseph Fourier - Grenoble 1 ( UJF ), Centre de Physique Théorique - UMR 7332 ( CPT ), Aix Marseille Université ( AMU ) -Université de Toulon ( UTLN ) -Centre National de la Recherche Scientifique ( CNRS ) -Aix Marseille Université ( AMU ) -Université de Toulon ( UTLN ) -Centre National de la Recherche Scientifique ( CNRS ), Leibniz-Institute for the Social Sciences [Mannheim] ( GESIS ), Aix Marseille Université ( AMU ) -Université de Toulon ( UTLN ) -Centre National de la Recherche Scientifique ( CNRS ), Laboratoire de l'Informatique du Parallélisme ( LIP ), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique ( Inria ) -Centre National de la Recherche Scientifique ( CNRS ), National Institute of Information and Communications Technology ( NICT ), Universitat Rovira i Virgili, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique ( CNRS ), Centre de Bioinformatique, Biostatistique et Biologie Intégrative ( C3BI ), Alfréd Rényi Institute of Mathematics, Hungarian Academy of Sciences (MTA), ISI Foundation, Aix-Marseille Université, Department of Computer Science, Computer Science Professors, National Institute of Information and Communications Technology, Sorbonne University, Centre National de la Recherche Scientifique (CNRS), Aalto-yliopisto, Aalto University, and Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)

Subjects
FOS: Computer and information sciences, Physics - Physics and Society, Discrete Mathematics (cs.DM), null models, FOS: Physical sciences, [ PHYS.PHYS.PHYS-SOC-PH ] Physics [physics]/Physics [physics]/Physics and Society [physics.soc-ph], Physics and Society (physics.soc-ph), [INFO.INFO-DM]Computer Science [cs]/Discrete Mathematics [cs.DM], Quantitative Biology - Quantitative Methods, [ PHYS.PHYS.PHYS-DATA-AN ] Physics [physics]/Physics [physics]/Data Analysis, Statistics and Probability [physics.data-an], Theoretical Computer Science, temporal networks, [ SDV.BIBS ] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Quantitative Methods (q-bio.QM), random graphs, Applied Mathematics, [PHYS.PHYS.PHYS-SOC-PH]Physics [physics]/Physics [physics]/Physics and Society [physics.soc-ph], [ INFO.INFO-DM ] Computer Science [cs]/Discrete Mathematics [cs.DM], [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], time-varying networks, Computational Mathematics, Physics - Data Analysis, Statistics and Probability, FOS: Biological sciences, Data Analysis, Statistics and Probability (physics.data-an), [PHYS.PHYS.PHYS-DATA-AN]Physics [physics]/Physics [physics]/Data Analysis, Statistics and Probability [physics.data-an], Computer Science - Discrete Mathematics
Abstract

Many dynamical systems can be successfully analyzed by representing them as networks. Empirically measured networks and dynamic processes that take place in these situations show heterogeneous, non-Markovian, and intrinsically correlated topologies and dynamics. This makes their analysis particularly challenging. Randomized reference models (RRMs) have emerged as a general and versatile toolbox for studying such systems. Defined as random networks with given features constrained to match those of an input (empirical) network, they may, for example, be used to identify important features of empirical networks and their effects on dynamical processes unfolding in the network. RRMs are typically implemented as procedures that reshuffle an empirical network, making them very generally applicable. However, the effects of most shuffling procedures on network features remain poorly understood, rendering their use nontrivial and susceptible to misinterpretation. Here we propose a unified framework for classifying and understanding microcanonical RRMs (MRRMs) that sample networks with uniform probability. Focusing on temporal networks, we survey applications of MRRMs found in the literature, and we use this framework to build a taxonomy of MRRMs that proposes a canonical naming convention, classifies them, and deduces their effects on a range of important network features. We furthermore show that certain classes of MRRMs may be applied in sequential composition to generate new MRRMs from the existing ones surveyed in this article. We finally provide a tutorial showing how to apply a series of MRRMs to analyze how different network features affect a dynamic process in an empirical temporal network., Comment: Accepted version

Published
2019

11. Link transmission centrality in large-scale social networks

Author

Márton Karsai, Qian Zhang, Alessandro Vespignani, Laboratory for the Modeling of Biological and Socio-technical Systems [Boston] (MoBS), Northeastern University [Boston], Dynamic Networks : Temporal and Structural Capture Approach (DANTE), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire de l'Informatique du Parallélisme (LIP), École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut Rhône-Alpin des systèmes complexes (IXXI), École normale supérieure - Lyon (ENS Lyon)-Université Lumière - Lyon 2 (UL2)-Université Jean Moulin - Lyon 3 (UJML), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Université Lumière - Lyon 2 (UL2)-Université Jean Moulin - Lyon 3 (UJML), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Centre National de la Recherche Scientifique (CNRS)-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École normale supérieure - Lyon (ENS Lyon)-Centre National de la Recherche Scientifique (CNRS)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École normale supérieure - Lyon (ENS Lyon)-Institut Rhône-Alpin des systèmes complexes (IXXI), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Université Lumière - Lyon 2 (UL2)-Université Jean Moulin - Lyon 3 (UJML), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Computational Epidemiology Laboratory (CERL), ISI Foundation Institute for Scientific Interchange, ANR-15-CE38-0011,SoSweet,Une sociolinguistique de Twitter : liens sociaux et variations linguistiques(2015), École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), and École normale supérieure de Lyon (ENS de Lyon)-Université Lumière - Lyon 2 (UL2)-Université Jean Moulin - Lyon 3 (UJML)

Subjects
FOS: Computer and information sciences, Physics - Physics and Society, Theoretical computer science, Computer science, FOS: Physical sciences, Physics and Society (physics.soc-ph), lcsh:Computer applications to medicine. Medical informatics, 01 natural sciences, Measure (mathematics), Social networks, [INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI], 010305 fluids & plasmas, 0103 physical sciences, Diffusion process, Weak tie, 010306 general physics, Diffusion processes, Social and Information Networks (cs.SI), Social network, business.industry, Scale (chemistry), [PHYS.PHYS.PHYS-SOC-PH]Physics [physics]/Physics [physics]/Physics and Society [physics.soc-ph], Computer Science - Social and Information Networks, Computer Science Applications, Computational Mathematics, Identification (information), Interpersonal ties, Transmission (telecommunications), Modeling and Simulation, Metric (mathematics), Link centrality measures, lcsh:R858-859.7, Centrality, business
Abstract

Understanding the importance of links in transmitting information in a network can provide ways to hinder or postpone ongoing dynamical phenomena like the spreading of epidemic or the diffusion of information. In this work, we propose a new measure based on stochastic diffusion processes, the \textit{transmission centrality}, that captures the importance of links by estimating the average number of nodes to whom they transfer information during a global spreading diffusion process. We propose a simple algorithmic solution to compute transmission centrality and to approximate it in very large networks at low computational cost. Finally we apply transmission centrality in the identification of weak ties in three large empirical social networks, showing that this metric outperforms other centrality measures in identifying links that drive spreading processes in a social network., 19 pages, 5 figures

Published
2018

12. Data Summaries and Representations: Definitions and Practical Use

Author

Alain Barrat, Ciro Cattuto, CPT - E5 Physique statistique et systèmes complexes, Centre de Physique Théorique - UMR 7332 (CPT), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Data Science Laboratory (ISI), and ISI Foundation Institute for Scientific Interchange

Subjects
Temporal network, Computer science, Data needs, Epidemic, Data-driven model, Data structure, MULTIPLEX, Data science, Variety (cybernetics), Use case, [PHYS.COND.CM-SM]Physics [physics]/Condensed Matter [cond-mat]/Statistical Mechanics [cond-mat.stat-mech], Representation (mathematics), Simple (philosophy)
Abstract

International audience; The chapter “Data Summaries and Representations: Definitions and Practical Use” examines data structures used to deal with complex networked data, using temporal networks as a concrete case. Complex networked data has become available in a variety of contexts, describing a variety of systems with growing abundance of details, such as, for instance, links between individuals in social networks, or the temporal evolution of these links. However, data needs to be summarized and represented in simple forms. This chapter describes several commonly used data summaries and levels of representation of temporal networks, as well as novel data representations that have been developed through the MULTIPLEX project. It focuses in particular on the case of temporal networks of contacts between individuals and shows in a series of concrete use cases how different representations can be used to characterize and compare data, or feed data-driven models of epidemic spreading processes.

Published
2018

13. Shifting patterns of seasonal influenza epidemics

Author

Vittoria Colizza, Thierry Blanchon, Pietro Coletti, Chiara Poletto, Clément Turbelin, Data Science Laboratory (ISI), ISI Foundation Institute for Scientific Interchange, Hasselt University (UHasselt), Institut Pierre Louis d'Epidémiologie et de Santé Publique (iPLESP), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM), Sorbonne Université (SU), and Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
0301 basic medicine, Time Factors, lcsh:Medicine, Influenza epidemics, Biology, Article, Seasonal influenza, 03 medical and health sciences, 0302 clinical medicine, Country level, Influenza, Human, Humans, 030212 general & internal medicine, Influenza, Clustering, Seasonality, Epidemics, lcsh:Science, Probability, 030304 developmental biology, 0303 health sciences, Multidisciplinary, Geography, Incidence, lcsh:R, Spatiotemporal pattern, 3. Good health, 030104 developmental biology, lcsh:Q, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, France, Seasons, Cartography
Abstract

Seasonal waves of influenza display a complex spatiotemporal pattern resulting from the interplay of biological, sociodemographic, and environmental factors. At country level many studies characterized the robust properties of annual epidemics, depicting a typical season. Here we analyzed season-by-season variability, introducing a clustering approach to assess the deviations from typical spreading patterns. The classification is performed on the similarity of temporal configurations of onset and peak times of regional epidemics, based on influenza-like-illness time-series in France from 1984 to 2014. We observed a larger variability in the onset compared to the peak. Two relevant classes of clusters emerge: groups of seasons sharing similar recurrent spreading patterns (clustered seasons) and single seasons displaying unique patterns (monoids). Recurrent patterns exhibit a more pronounced spatial signature than unique patterns. We assessed how seasons shift between these classes from onset to peak depending on epidemiological, environmental, and socio-demographic variables. We found that the spatial dynamics of influenza and its association with commuting, previously observed as a general property of French influenza epidemics, apply only to seasons exhibiting recurrent patterns. The proposed methodology is successful in providing new insights on influenza spread and can be applied to incidence time-series of different countries and different diseases. The work was partially supported by the EC-Health contract no. 278433 (PREDEMICS) to PC, CP and VC and by the French ANR project HarMS-flu (ANR-12-MONU-0018) to PC and VC. PC also acknowledges partial support from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement 682540 - TransMID).

Published
2018

14. Simplicial Activity Driven Model

Author

Giovanni Petri, Alain Barrat, ISI Foundation Institute for Scientific Interchange, CPT - E5 Physique statistique et systèmes complexes, Centre de Physique Théorique - UMR 7332 (CPT), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), and Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Physics - Physics and Society, Theoretical computer science, Simplex, Computer science, Complex system, General Physics and Astronomy, FOS: Physical sciences, Physics and Society (physics.soc-ph), 01 natural sciences, Outcome (probability), 010305 fluids & plasmas, Simple (abstract algebra), 0103 physical sciences, Pairwise comparison, [PHYS.COND.CM-SM]Physics [physics]/Condensed Matter [cond-mat]/Statistical Mechanics [cond-mat.stat-mech], 010306 general physics, Representation (mathematics), Block (data storage), Network model
Abstract

Many complex systems find a convenient representation in terms of networks: structures made by pairwise interactions (links) of elements (nodes). For many biological and social systems, elementary interactions involve however more than two elements, and simplicial complexes are more adequate to describe such phenomena. Moreover, these interactions often change over time. Here, we propose a framework to model such an evolution: the Simplicial Activity Driven (SAD) model, in which the building block is a simplex of nodes representing a multi-agent interaction. We show analytically and numerically that the use of simplicial structures leads to crucial differences in the outcome of paradigmatic processes modelling disease propagation or social contagion, with respect to the activity-driven (AD) model, a paradigmatic temporal network model involving only binary interactions. In particular, fluctuations in the number of nodes involved in the interactions can affect the outcome of models of simple contagion processes, contrarily to what happens in the AD model. Moreover, social contagion models such as cascading processes present a much richer phenomenology and can become extremely slow when occurring on evolving simplicial complexes., 20 pages, 3 figures (11 figures in supplementary)

Published
2018

15. Finding Events in Temporal Networks: Segmentation Meets Densest-Subgraph Discovery

Author

Aristides Gionis, Nikolaj Tatti, Francesco Bonchi, Polina Rozenshtein, Mauro Sozio, Data Science Laboratory (ISI), ISI Foundation Institute for Scientific Interchange, Eurecat - Centro Tecnológico de Catalunya, Aalto University, Département Informatique et Réseaux (INFRES), Télécom ParisTech, Data, Intelligence and Graphs (DIG), Laboratoire Traitement et Communication de l'Information (LTCI), Institut Mines-Télécom [Paris] (IMT)-Télécom Paris-Institut Mines-Télécom [Paris] (IMT)-Télécom Paris, Institut Mines-Télécom [Paris] (IMT)-Télécom Paris, Department of Information and Computer Science [Aalto], Helsinki Institute for Information Technology (HIIT), and Aalto University-University of Helsinki-Aalto University-University of Helsinki

Subjects
Social and Information Networks (cs.SI), FOS: Computer and information sciences, ta113, Sequence, Polynomial, Optimization problem, Computer science, Approximation algorithm, Timeline, Computer Science - Social and Information Networks, 02 engineering and technology, Graph, Dynamic programming, Set (abstract data type), 020204 information systems, Computer Science - Data Structures and Algorithms, 0202 electrical engineering, electronic engineering, information engineering, Data Structures and Algorithms (cs.DS), 020201 artificial intelligence & image processing, [INFO]Computer Science [cs], Greedy algorithm, Algorithm
Abstract

International audience; In this paper we study the problem of discovering a timeline of events in a temporal network. We model events as dense subgraphs that occur within intervals of network activity. We formulate the event-discovery task as an optimization problem, where we search for a partition of the network timeline into k non-overlapping intervals, such that the intervals span subgraphs with maximum total density. The output is a sequence of dense subgraphs along with corresponding time intervals, capturing the most interesting events during the network lifetime. A naïve solution to our optimization problem has polynomial but prohibitively high running time complexity. We adapt existing recent work on dynamic densest-subgraph discovery and approximate dynamic programming to design a fast approximation algorithm. Next, to ensure richer structure, we adjust the problem formulation to encourage coverage of a larger set of nodes. This problem is NP-hard even for static graphs. However, on static graphs a simple greedy algorithm leads to approximate solution due to submodularity. We extended this greedy approach for the case of temporal networks. However, the approximation guarantee does not hold. Nevertheless, according to the experiments, the algorithm finds good quality solutions.

Published
2018
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16. Mining (maximal) span-cores from temporal networks

Author

Francesco Bonchi, Edoardo Galimberti, Alain Barrat, Ciro Cattuto, Francesco Gullo, CPT - E5 Physique statistique et systèmes complexes, Centre de Physique Théorique - UMR 7332 (CPT), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Yahoo! Research [Barcelona], Data Science Laboratory (ISI), and ISI Foundation Institute for Scientific Interchange

Subjects
Social and Information Networks (cs.SI), FOS: Computer and information sciences, Physics - Physics and Society, Computer science, Property (programming), [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], FOS: Physical sciences, Computer Science - Social and Information Networks, 02 engineering and technology, Physics and Society (physics.soc-ph), Span (engineering), [INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI], Vertex (geometry), Task (project management), Identification (information), Quadratic equation, 020204 information systems, Computer Science - Data Structures and Algorithms, Core (graph theory), 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Data Structures and Algorithms (cs.DS), [PHYS.COND.CM-SM]Physics [physics]/Condensed Matter [cond-mat]/Statistical Mechanics [cond-mat.stat-mech], Focus (optics), Algorithm
Abstract

International audience; When analyzing temporal networks, a fundamental task is the identification of dense structures (i.e., groups of vertices that exhibit a large number of links), together with their temporal span (i.e., the period of time for which the high density holds). We tackle this task by introducing a notion of temporal core decomposition where each core is associated with its span: we call such cores span-cores. As the total number of time intervals is quadratic in the size of the temporal domain T under analysis, the total number of span-cores is quadratic in |T | as well. Our first contribution is an algorithm that, by exploiting containment properties among span-cores, computes all the span-cores efficiently. Then, we focus on the problem of finding only the maximal span-cores, i.e., span-cores that are not dominated by any other span-core by both the coreness property and the span. We devise a very efficient algorithm that exploits theoretical findings on the maximality condition to directly compute the maximal ones without computing all span-cores. Experimentation on several real-world temporal networks confirms the efficiency and scalability of our methods. Applications on temporal networks, gathered by a proximity-sensing infrastructure recording face-to-face interactions in schools, highlight the relevance of the notion of (maximal) span-core in analyzing social dynamics and detecting/correcting anomalies in the data.

Published
2018
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17. Attention on Weak Ties in Social and Communication Networks

Author

Alessandro Flammini, Filippo Menczer, Lilian Weng, Nicola Perra, Márton Karsai, LinkedIn Corporation, Dynamic Networks : Temporal and Structural Capture Approach (DANTE), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire de l'Informatique du Parallélisme (LIP), École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut Rhône-Alpin des systèmes complexes (IXXI), École normale supérieure de Lyon (ENS de Lyon)-Université Lumière - Lyon 2 (UL2)-Université Jean Moulin - Lyon 3 (UJML), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Université Lumière - Lyon 2 (UL2)-Université Jean Moulin - Lyon 3 (UJML), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), École normale supérieure de Lyon (ENS de Lyon), University of Greenwich, Indiana University [Bloomington], Indiana University System, ISI Foundation Institute for Scientific Interchange, École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), École normale supérieure - Lyon (ENS Lyon)-Université Lumière - Lyon 2 (UL2)-Université Jean Moulin - Lyon 3 (UJML), and École normale supérieure - Lyon (ENS Lyon)

Subjects
0301 basic medicine, Scrutiny, Computer science, Interpretation (philosophy), 02 engineering and technology, Object (philosophy), [INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI], Microeconomics, 03 medical and health sciences, Interpersonal ties, 030104 developmental biology, Empirical research, 020204 information systems, Scale (social sciences), 0202 electrical engineering, electronic engineering, information engineering, Social media, Empirical evidence, Social psychology, ComputingMilieux_MISCELLANEOUS
Abstract

Granovetter’s weak tie theory of social networks is built around two central hypotheses. The first states that strong social ties carry the large majority of interaction events; the second maintains that weak social ties, although less active, are often relevant for the exchange of especially important information (e.g., about potential new jobs in Granovetter’s work). While several empirical studies have provided support for the first hypothesis, the second has been the object of far less scrutiny. A possible reason is that it involves notions relative to the nature and importance of the information that are hard to quantify and measure, especially in large scale studies. Here, we search for empirical validation of both Granovetter’s hypotheses. We find clear empirical support for the first. We also provide empirical evidence and a quantitative interpretation for the second. We show that attention, measured as the fraction of interactions devoted to a particular social connection, is high on weak ties—possibly reflecting the postulated informational purposes of such ties—but also on very strong ties. Data from online social media and mobile communication reveal network-dependent mixtures of these two effects on the basis of a platform’s typical usage. Our results establish a clear relationships between attention, importance, and strength of social links, and could lead to improved algorithms to prioritize social media content.

Published
2018

18. Estimating the epidemic risk using non-uniformly sampled contact data

Author

Fournet, Julie, Barrat, Alain, CPT - E5 Physique statistique et systèmes complexes, Centre de Physique Théorique - UMR 7332 (CPT), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Data Science Laboratory (ISI), and ISI Foundation Institute for Scientific Interchange

Subjects
Physics - Physics and Society, lcsh:R, FOS: Physical sciences, lcsh:Medicine, Physics and Society (physics.soc-ph), Risk Assessment, Article, Physics - Data Analysis, Statistics and Probability, Disease Transmission, Infectious, Humans, lcsh:Q, Contact Tracing, [PHYS.COND.CM-SM]Physics [physics]/Condensed Matter [cond-mat]/Statistical Mechanics [cond-mat.stat-mech], lcsh:Science, Epidemics, Epidemiologic Methods, Data Analysis, Statistics and Probability (physics.data-an)
Abstract

International audience; Many datasets describing contacts in a population suffer from incompleteness due to population sampling and underreporting of contacts. Data-driven simulations of spreading processes using such incomplete data lead to an underestimation of the epidemic risk, and it is therefore important to devise methods to correct this bias. We focus here on a non-uniform sampling of the contacts between individuals, aimed at mimicking the results of diaries or surveys, and consider as case studies two datasets collected in different contexts. We show that using surrogate data built using a method developed in the case of uniform population sampling yields an improvement with respect to the use of the sampled data but is strongly limited by the underestimation of the link density in the sampled network. We put forward a second method to build surrogate data that assumes knowledge of the density of links within one of the groups forming the population. We show that it gives very good results when the population is strongly structured, and discuss its limitations in the case of a population with a weaker group structure. These limitations highlight the interest of measurements using wearable sensors able to yield accurate information on the structure and durations of contacts.

Published
2017

19. Epidemic Threshold in Continuous-Time Evolving Networks

Author

Eugenio Valdano, Michele Re Fiorentin, Vittoria Colizza, Chiara Poletto, Institut Pierre Louis d'Epidémiologie et de Santé Publique (iPLESP), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU), Istituto Italiano di Tecnologia (IIT), ISI Foundation Institute for Scientific Interchange, and Gestionnaire, HAL Sorbonne Université 5

Subjects
Physics - Physics and Society, Discretization, Computer science, [SDV]Life Sciences [q-bio], Populations and Evolution (q-bio.PE), General Physics and Astronomy, Propagator, FOS: Physical sciences, Polymer, Soft Matter, Biological, Climate, and Interdisciplinary Physics, Physics and Society (physics.soc-ph), 01 natural sciences, 010305 fluids & plasmas, [SDV] Life Sciences [q-bio], Formalism (philosophy of mathematics), Evolving networks, Scale separation, FOS: Biological sciences, 0103 physical sciences, Letters, Statistical physics, Commutation, 010306 general physics, Quantitative Biology - Populations and Evolution
Abstract

Current understanding of the critical outbreak condition on temporal networks relies on approximations (time scale separation, discretization) that may bias the results. We propose a theoretical framework to compute the epidemic threshold in continuous time through the infection propagator approach. We introduce the {\em weak commutation} condition allowing the interpretation of annealed networks, activity-driven networks, and time scale separation into one formalism. Our work provides a coherent connection between discrete and continuous time representations applicable to realistic scenarios., 13 pages, 2 figures

Published
2017

20. Estimating the outcome of spreading processes on networks with incomplete information: a mesoscale approach

Author

Sapienza, Anna, Barrat, Alain, Cattuto, Ciro, Gauvin, Laetitia, Information Sciences Institute [California], University of Southern California (USC), Data Science Laboratory (ISI), ISI Foundation Institute for Scientific Interchange, CPT - E5 Physique statistique et systèmes complexes, Centre de Physique Théorique - UMR 7332 (CPT), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), and Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)

Subjects
missing data, Physics - Physics and Society, tensor decomposition, joint factorization, Temporal network, FOS: Physical sciences, Physics and Society (physics.soc-ph), [PHYS.COND.CM-SM]Physics [physics]/Condensed Matter [cond-mat]/Statistical Mechanics [cond-mat.stat-mech], partial information
Abstract

International audience; Recent advances in data collection have facilitated the access to time-resolved human proximity data that can conveniently be represented as temporal networks of contacts between individuals. While this type of data is fundamental to investigate how information or diseases propagate in a population, it often suffers from incompleteness, which possibly leads to biased conclusions. A major challenge is thus to estimate the outcome of spreading processes occurring on temporal networks built from partial information. To cope with this problem, we devise an approach based on Non-negative Tensor Factorization (NTF)-a dimensionality reduction technique from multi-linear algebra. The key idea is to learn a low-dimensional representation of the temporal network built from partial information, to adapt it to take into account temporal and structural heterogeneity properties known to be crucial for spreading processes occurring on networks, and to construct in this way a surrogate network similar to the complete original network. To test our method, we consider several human-proximity networks, on which we simulate a loss of data. Using our approach on the resulting partial networks, we build a surrogate version of the complete network for each. We then compare the outcome of a spreading process on the complete networks (non altered by a loss of data) and on the surrogate networks. We observe that the epidemic sizes obtained using the surrogate networks are in good agreement with those measured on the complete networks. Finally, we propose an extension of our framework when additional data sources are available to cope with the missing data problem.

Published
2017
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21. Healthcare-seeking behaviour in case of influenza-like illness in the French general population and factors associated with a GP consultation: an observational prospective study

Author

Ariza, Matthieu, Guerrisi, Caroline, Souty, Cécile, Rossignol, Louise, Turbelin, Clément, Hanslik, Thomas, Colizza, Vittoria, Blanchon, Thierry, Sorbonne Université - Faculté de Médecine (SU FM), Sorbonne Université (SU), Institut Pierre Louis d'Epidémiologie et de Santé Publique (iPLESP), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Hôpital Ambroise Paré [AP-HP], and ISI Foundation Institute for Scientific Interchange

Subjects
general practice, lcsh:R5-920, web-based study, Research, influenza-like illness, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, healthcare seeking behaviour, lcsh:Medicine (General), general population
Abstract

International audience; Background: GP consultation rates for influenza-like illness (ILI) are poorly known in France and there is a paucity of literature on this topic. In the few articles that have been published, the results are heterogeneous.Aim: The aim of the present study was to estimate the proportion of ILI inducing a GP consultation, and to assess its determinants.Design & setting: Participants of a French web-based cohort study who reported ≥1 ILI episode between 2012 and 2015 were included. Sociodemographic characteristics, access to health care, and health status variables were collected.Method: Healthcare-seeking behaviour was analysed and factors associated with a GP consultation identified using a conditional logistic regression.Results: Of the 6023 ILI episodes reported, 1961 (32.6%) led to a GP consultation, with no difference between those at risk of influenza complications and those not (P = 0.42). A GP consultation was more frequent for individuals living in a rural area (odds ratio [OR] = 1.21, 95% confidence interval [CI] = 1.02 to 1.43); those with a lower educational level (OR = 1.43, 95% CI = 1.18 to 1.74); those using the internet to find information about influenza (OR = 1.63, 95% CI = 1.30 to 2.03); patients presenting with worrying symptoms (fever, cough, dyspnoea, sputum, or asthenia); patients having a negative perception of their own health status (OR = 1.51, 95% CI = 1.07 to 2.13; and those having declared a personal doctor (OR = 2.86, 95% CI = 1.72 to 4.76). A GP consultation was less frequent for individuals using alternative medicine (OR = 0.68, 95% CI = 0.58 to 0.78).Conclusion: This study allows the identification of specific factors associated with GP consultation for an ILI episode. These findings may help to coordinate health information campaigns and to raise awareness, especially among individuals at risk of influenza complications.

Published
2017

22. Robust modeling of human contact networks across different scales and proximity-sensing techniques

Author

Starnini, Michele, Lepri, Bruno, Baronchelli, Andrea, Barrat, Alain, Cattuto, C., Pastor-Satorras, Romualdo, Departament de Fisica i Enginyeria Nuclear (DEPARTAMENT DI FISICA), Universitat Politècnica de Catalunya [Barcelona] (UPC), Fondazione Bruno Kessler [Trento, Italy] (FBK), Department of Mathematics, City University London, CPT - E5 Physique statistique et systèmes complexes, Centre de Physique Théorique - UMR 7332 (CPT), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Data Science Laboratory (ISI), and ISI Foundation Institute for Scientific Interchange

Subjects
Wearable Sensors, QA75, Social and Information Networks (cs.SI), FOS: Computer and information sciences, Physics - Physics and Society, Mathematical Modeling, FOS: Physical sciences, Computer Science - Social and Information Networks, Physics and Society (physics.soc-ph), Social Computing, Computational Social Science, [PHYS.COND.CM-SM]Physics [physics]/Condensed Matter [cond-mat]/Statistical Mechanics [cond-mat.stat-mech], Social Network Analysis, Mobile Sensing
Abstract

International audience; The problem of mapping human close-range proximity networks has been tackled using a variety of technical approaches. Wearable electronic devices, in particular, have proven to be particularly successful in a variety of settings relevant for research in social science, complex networks and infectious diseases dynamics. Each device and technology used for proximity sensing (e.g., RFIDs, Bluetooth, low-power radio or infrared communication, etc.) comes with specific biases on the close-range relations it records. Hence it is important to assess which statistical features of the empirical proximity networks are robust across different measurement techniques, and which modeling frameworks generalize well across empirical data. Here we compare time-resolved proximity networks recorded in different experimental settings and show that some important statistical features are robust across all settings considered. The observed universality calls for a simplified modeling approach. We show that one such simple model is indeed able to reproduce the main statistical distributions characterizing the empirical temporal networks.

Published
2017
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23. Contact diaries versus wearable proximity sensors in measuring contact patterns at a conference: method comparison and participants’ attitudes

Author

Timo Smieszek, Gérard Krause, Ciro Cattuto, Stefanie Castell, Alain Barrat, Peter J White, Imperial College London, Helmholtz Centre for Infection Research (HZI), CPT - E5 Physique statistique et systèmes complexes, Centre de Physique Théorique - UMR 7332 (CPT), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Data Science Laboratory (ISI), ISI Foundation Institute for Scientific Interchange, Leibniz Forschungsinstitut für Molekulare Pharmakolgie = Leibniz Institute for Molecular Pharmacology [Berlin, Allemagne] (FMP), Leibniz Association, UK National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Modelling Methodology at Imperial College London in partnership with Public Health England (PHE) for funding (grant HPRU-2012-10080), ANR-12-MONU-0018,HarMS-flu,Approches multi-échelles pour la modélisation de la propagation de la grippe pilotée par les données.(2012), ANR-11-IDEX-0001,Amidex,INITIATIVE D'EXCELLENCE AIX MARSEILLE UNIVERSITE(2011), and Helmholtz Centre for infection research, Inhoffenstr. 7, 38124 Braunschweig, Germany.

Subjects
Male, 0301 basic medicine, Contact diary, Applied psychology, Wearable computer, Biosensing Techniques, Contact network, Medical Records, 0302 clinical medicine, Acceptability, 1108 Medical Microbiology, Surveys and Questionnaires, Proximity sensor, Medicine, 030212 general & internal medicine, [PHYS.COND.CM-SM]Physics [physics]/Condensed Matter [cond-mat]/Statistical Mechanics [cond-mat.stat-mech], Infection transmission, RFID, education.field_of_study, Infectious disease, Middle Aged, 16. Peace & justice, Mobile Applications, 3. Good health, Infectious Diseases, Network epidemiology, Method comparison, Female, Research Article, 0605 Microbiology, Adult, Concordance, Population, Fitness Trackers, Microbiology, Young Adult, 03 medical and health sciences, Measurement error, Humans, Family, education, business.industry, Social Support, 1103 Clinical Sciences, Actigraphy, Congresses as Topic, Network model, 030104 developmental biology, Attitude, Self Report, Contact Tracing, business, Contact tracing
Abstract

Background Studies measuring contact networks have helped to improve our understanding of infectious disease transmission. However, several methodological issues are still unresolved, such as which method of contact measurement is the most valid. Further, complete network analysis requires data from most, ideally all, members of a network and, to achieve this, acceptance of the measurement method. We aimed at investigating measurement error by comparing two methods of contact measurement – paper diaries vs. wearable proximity sensors – that were applied concurrently to the same population, and we measured acceptability. Methods We investigated the contact network of one day of an epidemiology conference in September 2014. Seventy-six participants wore proximity sensors throughout the day while concurrently recording their contacts with other study participants in a paper-diary; they also reported on method acceptability. Results There were 329 contact reports in the paper diaries, corresponding to 199 contacts, of which 130 were noted by both parties. The sensors recorded 316 contacts, which would have resulted in 632 contact reports if there had been perfect concordance in recording. We estimated the probabilities that a contact was reported in a diary as: P = 72 % for 60 min. The sets of sensor-measured and self-reported contacts had a large intersection, but neither was a subset of the other. Participants’ aggregated contact duration was mostly substantially longer in the diary data than in the sensor data. Twenty percent of respondents (>1 reported contact) stated that filling in the diary was too much work, 25 % of respondents reported difficulties in remembering contacts, and 93 % were comfortable having their conference contacts measured by sensors. Conclusion Reporting and recording were not complete; reporting was particularly incomplete for contacts

Published
2016

24. Evaluating the epidemic risk from incomplete data

Author

Génois, Mathieu, Vestergaard, Christian, Barrat, Alain, Centre de Physique Théorique - UMR 7332 (CPT), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), CPT - E5 Physique statistique et systèmes complexes, Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Data Science Laboratory (ISI), and ISI Foundation Institute for Scientific Interchange

Subjects
[PHYS.COND.CM-SM]Physics [physics]/Condensed Matter [cond-mat]/Statistical Mechanics [cond-mat.stat-mech]
Abstract

International audience; Les simulations numériques de propagations de maladies infectieuses deviennent de plus en plus détaillées. Des progrès théoriques montrent comment des données même incomplètes peuvent être utilisées dans les modèles.

Published
2016

25. The Contribution of Risk Factors to the Higher Incidence of Invasive and In Situ Breast Cancers in Women With Higher Levels of Education in the European Prospective Investigation Into Cancer and Nutrition

Author

Elio Riboli, Carla H. van Gils, Gillian K Reeves, Tonje Braaten, María José Sánchez, Kim Overvad, Naomi E. Allen, Amelia Mattiello, Eric J. Duell, Anton E. Kunst, Petra H.M. Peeters, Kay-Tee Khaw, Anne-Kathrin Illner, Domenico Palli, Pagona Lagiou, Valentina Gallo, Anja Olsen, Eiliv Lund, Eva Ardanaz, José Ramón Quirós, Signe Borgquist, Manuela M. Bergmann, Paolo Vineis, Franco Berrino, Jonas Manjer, Carmen Navarro, Veronique Chajes, Rudolf Kaaks, Sabina Rinaldi, Carlotta Sacerdote, Silke Hermann, Rosario Tumino, A. M. May, Antonia Trichopoulou, Dimitrios Trichopoulos, Evelyn M. Monninkhof, Anne Tjønneland, Gwenn Menvielle, Hendriek Boshuizen, H. Bas Bueno-de-Mesquita, Nadia Slimani, Amsterdam Public Health, Public and occupational health, Department of Public Health, Erasmus University Medical Center [Rotterdam] (Erasmus MC), Centre de recherche en épidémiologie et santé des populations (CESP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Université Paris-Sud - Paris 11 (UP11)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), National Institute for Public Health and the Environment [Bilthoven] (RIVM), University of Amsterdam [Amsterdam] (UvA), Julius Center for Health Sciences and Primary Care, University Medical Center [Utrecht], Dept of Epidemiology and Public Health, Imperial College London, Department of Cardiology and Department of Clinical Epidemiology, Aarhus University Hospital, Cancer Epidemiology Institute, Danish Cancer Society, Division of Cancer Epidemiology, German Cancer Research Center - Deutsches Krebsforschungszentrum [Heidelberg] (DKFZ), Dept of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbrücke (DIfE), Dept of Hygiene, Epidemiology and Medical Statistics, University of Athens Medical School [Athens], Department of Epidemiology, Havard School of Public Health, Hellenic Health Foundation, Molecular and Nutritional Epidemiology Unit, Cancer Research and Prevention Institute – ISPO, Department of Preventive & Predictive Medicine, Fondazione IRCCS-Istituto Nazionale dei Tumori, Department of Clinical and Experimental Medicine, Università degli studi di Napoli Federico II, Cancer Registry and Histopathology Unit, Civile - M.P.Arezzo Hospital, CPO Piemonte, Institute of Community Medicine, University of Tromsø (UiT), Public Health and Health Planning Directorate, Unit of Nutrition, Environment, and Cancer, Catalan Institute of Oncology, Andalusian School of Public Health [Granada], CIBERESP, CIBER Epidemiologia y Salud Pública, Epidemiology Department, Murcia Health Council, Public Health Institute of Navarra, Department of Oncology, Lund University Hospital-Lund University [Lund], Department of surgery, Lund University [Lund]-Malmö University Hospital, Dept of Public Health and Primary Care, University of Cambridge [UK] (CAM)-MRC Center for Nutritional Epidemiology and Cancer Prevention and Survival, Cancer Epidemiology Unit, University of Oxford [Oxford]-Cancer Epidemiology Unit, International Agency for Cancer Research (IACR), University of Torino and CPO-Piemonte, Università degli studi di Torino (UNITO), ISI Foundation Institute for Scientific Interchange, G Menvielle received a funding from the Fondation pour la Recherche Médicale for this analysis. The project was in part funded by the European Commission, through the Eurocadet project (from the commission of the European communities research directorate-general, grant No EUROCADET:SP23-CT-2005-006528). EPIC was supported by the European Commission: Public Health and Consumer Protection Directorate 1993-2004 and the Research Directorate-General 2005-2008. European Commission FP5 project (QLG1-CT-2001-01049). The EPIC study was funded by 'Europe Against Cancer' Programme of the European Commission (SANCO), Ligue contre le Cancer (France), Société 3M (France), Mutuelle Générale de l'Education Nationale, Institut National de la Santé et de la Recherche Médicale, German Cancer Aid, German Cancer Research Center, German Federal Ministry of Education and Research, Red Temática de Investigación Cooperativa de Centros de Cáncer (C03/10), the participating regional governments and institutions of Murcia, Navarra, Asturias, Pais Vasco y Andalucia, Spain, Cancer Research UK, Medical Research Council, United Kingdom, Stroke Association, United Kingdom, British Heart Foundation, Department of Health, United Kingdom, Food Standards Agency, United Kingdom, The Wellcome Trust, United Kingdom, Greek Ministry of Health, Stavros Niarchos Foundation, Italian Association for Research on Cancer, Dutch Ministry of Public Health, Welfare and Sports, Dutch Ministry of Health, Dutch Prevention Funds, LK Research Funds, Dutch Zorg Onderzoek Nederland, World Cancer Research Fund, Swedish Cancer Society, Swedish Scientific Council, Regional Government of Vasterbotten and Skane, Sweden, Norwegian Cancer Society, and Foundation to Promote Research into Functional Vitamin B12 Deficiency, Norway. Some authors are partners of Environmental Cancer Risk, Nutrition and Individual Susceptibility, a network of excellence of the European Commission (6FP contract 513943). Antonio Agudo and Paolo Vineis were supported by ECNIS., Schmaus, Annie, Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Université Paris-Sud - Paris 11 (UP11)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM), University of Naples Federico II = Università degli studi di Napoli Federico II, Lund University [Lund]-Lund University Hospital, University of Oxford-Cancer Epidemiology Unit, Università degli studi di Torino = University of Turin (UNITO), Public Health, and Cell biology

Subjects
MESH: Reproductive History, Epidemiology, MESH: Risk Assessment, 0302 clinical medicine, MESH: Risk Factors, Prevalence, breast neoplasms, Mass Screening, risk factors, Prospective Studies, 030212 general & internal medicine, MESH: Incidence, Prospective cohort study, reproductive history, MESH: Aged, education, MESH: Middle Aged, Obstetrics, Incidence (epidemiology), MESH: Follow-Up Studies, Middle Aged, Nutrition Surveys, Prognosis, 3. Good health, European Prospective Investigation into Cancer and Nutrition, Europe, 030220 oncology & carcinogenesis, Educational Status, Female, Breast disease, Risk assessment, Adult, medicine.medical_specialty, Risk Assessment, Article, MESH: Prognosis, 03 medical and health sciences, Breast cancer, SDG 3 - Good Health and Well-being, MESH: Nutrition Surveys, medicine, Humans, Neoplasm Invasiveness, MESH: Mass Screening, Risk factor, MESH: Prevalence, Aged, Gynecology, MESH: Humans, business.industry, Cancer, MESH: Adult, MESH: Neoplasm Invasiveness, medicine.disease, MESH: Prospective Studies, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, incidence, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, MESH: Europe, business, MESH: Educational Status, MESH: Female, MESH: Breast Neoplasms, Follow-Up Studies
Abstract

International audience; The authors investigated the role of known risk factors in educational differences in breast cancer incidence. Analyses were based on the European Prospective Investigation Into Cancer and Nutrition and included 242,095 women, 433 cases of in situ breast cancer, and 4,469 cases of invasive breast cancer. Reproductive history (age at first full-term pregnancy and parity), exposure to endogenous and exogenous hormones, height, and health behaviors were accounted for in the analyses. Relative indices of inequality (RII) for education were estimated using Cox regression models. A higher risk of invasive breast cancer was found among women with higher levels of education (RII = 1.22, 95% confidence interval (CI): 1.09, 1.37). This association was not observed among nulliparous women (RII = 1.13, 95% CI: 0.84, 1.52). Inequalities in breast cancer incidence decreased substantially after adjusting for reproductive history (RII = 1.11, 95% CI: 0.98, 1.25), with most of the association being explained by age at first full-term pregnancy. Each other risk factor explained a small additional part of the inequalities in breast cancer incidence. Height accounted for most of the remaining differences in incidence. After adjusting for all known risk factors, the authors found no association between education level and risk of invasive breast cancer. Inequalities in incidence were more pronounced for in situ breast cancer, and those inequalities remained after adjustment for all known risk factors (RII = 1.61, 95% CI: 1.07, 2.41), especially among nulliparous women.

Published
2011
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26. Compensating for population sampling in simulations of epidemic spread on temporal contact networks

Author

Ciro Cattuto, Mathieu Génois, Alain Barrat, Christian L. Vestergaard, CPT - E5 Physique statistique et systèmes complexes, Centre de Physique Théorique - UMR 7332 (CPT), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Data Science Laboratory (ISI), ISI Foundation Institute for Scientific Interchange, ANR-12-MONU-0018,HarMS-flu,Approches multi-échelles pour la modélisation de la propagation de la grippe pilotée par les données.(2012), and European Project

Subjects
FOS: Computer and information sciences, Physics - Physics and Society, Computer science, Population, FOS: Physical sciences, General Physics and Astronomy, Context (language use), Physics and Society (physics.soc-ph), computer.software_genre, Communicable Diseases, 01 natural sciences, Article, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, Resampling, 0103 physical sciences, Humans, [PHYS.COND.CM-SM]Physics [physics]/Condensed Matter [cond-mat]/Statistical Mechanics [cond-mat.stat-mech], 010306 general physics, education, Set (psychology), 030304 developmental biology, Social and Information Networks (cs.SI), Estimation, 0303 health sciences, education.field_of_study, Multidisciplinary, Series (mathematics), Sampling (statistics), Computer Science - Social and Information Networks, General Chemistry, Models, Theoretical, Outcome (probability), Data mining, Contact Tracing, computer, Algorithms
Abstract

Data describing human interactions often suffer from incomplete sampling of the underlying population. As a consequence, the study of contagion processes using data-driven models can lead to a severe underestimation of the epidemic risk. Here we present a systematic method to alleviate this issue and obtain a better estimation of the risk in the context of epidemic models informed by high-resolution time-resolved contact data. We consider several such data sets collected in various contexts and perform controlled resampling experiments. We show how the statistical information contained in the resampled data can be used to build a series of surrogate versions of the unknown contacts. We simulate epidemic processes on the resulting reconstructed data sets and show that it is possible to obtain good estimates of the outcome of simulations performed using the complete data set. We discuss limitations and potential improvements of our method., Data describing human interaction suffer from incomplete sampling of population, affecting the reliability of studies on contagion spreading. Here, the authors present a systematic method to resample data and generate surrogate datasets of the unknown contacts, which approximate well the complete datasets.

Published
2015

27. A mathematical model for voice leading and its complexity

Author

Bergomi, Mattia Giuseppe, Jadanza, Riccardo, Portaluri, Alessandro, Représentations musicales (Repmus), Sciences et Technologies de la Musique et du Son (STMS), Institut de Recherche et Coordination Acoustique/Musique (IRCAM)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche et Coordination Acoustique/Musique (IRCAM)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Università degli Studi di Milano = University of Milan (UNIMI), ISI Foundation Institute for Scientific Interchange, Università degli studi di Torino = University of Turin (UNITO), Università degli Studi di Milano [Milano] (UNIMI), and Università degli studi di Torino (UNITO)

Subjects
[MATH.MATH-GM]Mathematics [math]/General Mathematics [math.GM], Computer Science::Sound, Mathematics - History and Overview, History and Overview (math.HO), FOS: Mathematics, [MATH]Mathematics [math], 00A65
Abstract

We give a formal definition of the musical concept of voice leading in mathematical terms, based on the idea of partial permutations of certain ordered multisets. Then we associate a partial permutation matrix with a voice leading in a unique way and write an algorithm to easily transform any musical composition into a sequence of such matrices; we then generalise it in order to include in the model also rhythmic independence and rests. From that we extract a vector whose components return information about the movements of the voices in the piece and hence about the complexity of the voice leading. We provide some examples by analysing three compositions, also visualising complexity as a point cloud for each piece. Finally, we interpret the sequence of complexity vectors associated with each composition, thus considering the position of each observation with respect to time. The Dynamic Time Warping allows us to compute the distance between two pieces and to show that our approach distinguishes well the examples that we took into account, exhibiting a strong indication that the notion of complexity we propose is a good tool to identify and classify musical pieces.

Published
2015

28. Attention on Weak Ties in Social and Communication Networks

Author

Weng, Lilian, Karsai, Márton, Perra, Nicola, Menczer, Filippo, Flammini, Alessandro, LinkedIn Corporation, School of Informatics and Computing, Indiana University [Bloomington], Indiana University [Bloomington], Indiana University System-Indiana University System, Dynamic Networks : Temporal and Structural Capture Approach (DANTE), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire de l'Informatique du Parallélisme (LIP), École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut Rhône-Alpin des systèmes complexes (IXXI), École normale supérieure - Lyon (ENS Lyon)-Université Lumière - Lyon 2 (UL2)-Université Joseph Fourier - Grenoble 1 (UJF)-Université Jean Moulin - Lyon 3 (UJML), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Lumière - Lyon 2 (UL2)-Université Joseph Fourier - Grenoble 1 (UJF)-Université Jean Moulin - Lyon 3 (UJML), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Laboratory for the Modeling of Biological and Socio-technical Systems [Boston] (MoBS), Northeastern University [Boston], Data Science Laboratory (ISI), ISI Foundation Institute for Scientific Interchange, École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), and École normale supérieure de Lyon (ENS de Lyon)-Université Lumière - Lyon 2 (UL2)-Université Joseph Fourier - Grenoble 1 (UJF)-Université Jean Moulin - Lyon 3 (UJML)

Subjects
Social and Information Networks (cs.SI), FOS: Computer and information sciences, Physics - Physics and Society, FOS: Physical sciences, Computer Science - Social and Information Networks, Physics and Society (physics.soc-ph), [INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI]
Abstract

Granovetter's weak tie theory of social networks is built around two central hypotheses. The first states that strong social ties carry the large majority of interaction events; the second maintains that weak social ties, although less active, are often relevant for the exchange of especially important information (e.g., about potential new jobs in Granovetter's work). While several empirical studies have provided support for the first hypothesis, the second has been the object of far less scrutiny. A possible reason is that it involves notions relative to the nature and importance of the information that are hard to quantify and measure, especially in large scale studies. Here, we search for empirical validation of both Granovetter's hypotheses. We find clear empirical support for the first. We also provide empirical evidence and a quantitative interpretation for the second. We show that attention, measured as the fraction of interactions devoted to a particular social connection, is high on weak ties --- possibly reflecting the postulated informational purposes of such ties --- but also on very strong ties. Data from online social media and mobile communication reveal network-dependent mixtures of these two effects on the basis of a platform's typical usage. Our results establish a clear relationships between attention, importance, and strength of social links, and could lead to improved algorithms to prioritize social media content.

Published
2015

29. Osmotic stress affects functional properties of human melanoma cell lines

Author

Mikko J. Alava, Caterina A. M. La Porta, Anna Ghilardi, Maria Enrica Pasini, Stefano Zapperi, Martine Ben Amar, Lasse Laurson, Department of Biosciences [Milano], Università degli Studi di Milano [Milano] (UNIMI), COMP Centre of Excellence, Aalto University, Department of applied physics, Data Science Laboratory (ISI), ISI Foundation Institute for Scientific Interchange, Department of Physics [Milano], Università degli Studi di Milano-Bicocca [Milano] (UNIMIB), CNR-IENI Milano, Institut Universitaire de Cancérologie [Paris] (IUC), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Physique Statistique de l'ENS (LPS), Université Paris Diderot - Paris 7 (UPD7)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Fédération de recherche du Département de physique de l'Ecole Normale Supérieure - ENS Paris (FRDPENS), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Università degli Studi di Milano = University of Milan (UNIMI), Università degli Studi di Milano-Bicocca = University of Milano-Bicocca (UNIMIB), Institut Universitaire de Cancérologie [Sorbonne Université] (IUC), Fédération de recherche du Département de physique de l'Ecole Normale Supérieure - ENS Paris (FRDPENS), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[PHYS]Physics [physics], Osmotic shock, Chemistry, Cell growth, Melanoma, FOS: Physical sciences, General Physics and Astronomy, Cancer, Quantitative Biology - Tissues and Organs, Nanotechnology, PHENOTYPE, medicine.disease, ENDOTHELIAL-CELLS, Metastasis, Cell biology, HYDROSTATIC-PRESSURE, Biological Physics (physics.bio-ph), Cell culture, FOS: Biological sciences, Cancer cell, medicine, Osmotic pressure, Physics - Biological Physics, Tissues and Organs (q-bio.TO), [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology
Abstract

International audience; Understanding the role of microenvironment in cancer growth and metastasis is a key issue for cancer research. Here, we study the effect of osmotic pressure on the functional properties of primary and metastatic melanoma cell lines. In particular, we experimentally quantify individual cell motility and transmigration capability. We then perform a circular scratch assay to study how a cancer cell front invades an empty space. Our results show that primary melanoma cells are sensitive to a low osmotic pressure, while metastatic cells are less. To better understand the experimental results, we introduce and study a continuous model for the dynamics of a cell layer and a stochastic discrete model for cell proliferation and diffusion. The two models capture essential features of the experimental results and allow to make predictions for a wide range of experimentally measurable parameters.

Published
2015

30. Data on face-to-face contacts in an office building suggests a low-cost vaccination strategy based on community linkers

Author

André Panisson, Isabelle Bonmarin, Alain Barrat, Mathieu Génois, Christian L. Vestergaard, Julie Fournet, CPT - E5 Physique statistique et systèmes complexes, Centre de Physique Théorique - UMR 7332 (CPT), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Data Science Laboratory (ISI), ISI Foundation Institute for Scientific Interchange, Département des maladies infectieuses, Institut de Veille Sanitaire (INVS), ANR-12-MONU-0018,HarMS-flu,Approches multi-échelles pour la modélisation de la propagation de la grippe pilotée par les données.(2012), and European Project

Subjects
FOS: Computer and information sciences, Empirical data, Physics - Physics and Society, Sociology and Political Science, Social Psychology, Computer science, Epidemiology, Internet privacy, Complex networks, FOS: Physical sciences, Physics and Society (physics.soc-ph), Contact network, 01 natural sciences, [INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI], 03 medical and health sciences, Face-to-face, Betweenness centrality, [SDV.MHEP.MI]Life Sciences [q-bio]/Human health and pathology/Infectious diseases, 0103 physical sciences, Epidemic spread, 010306 general physics, [NLIN.NLIN-AO]Nonlinear Sciences [physics]/Adaptation and Self-Organizing Systems [nlin.AO], 030304 developmental biology, Social and Information Networks (cs.SI), 0303 health sciences, Sociophysics, business.industry, Communication, [PHYS.PHYS.PHYS-SOC-PH]Physics [physics]/Physics [physics]/Physics and Society [physics.soc-ph], Computer Science - Social and Information Networks, Temporal networks, Complex network, Vaccination, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, business
Abstract

Empirical data on contacts between individuals in social contexts play an important role in providing information for models describing human behavior and how epidemics spread in populations. Here, we analyze data on face-to-face contacts collected in an office building. The statistical properties of contacts are similar to other social situations, but important differences are observed in the contact network structure. In particular, the contact network is strongly shaped by the organization of the offices in departments, which has consequences in the design of accurate agent-based models of epidemic spread. We consider the contact network as a potential substrate for infectious disease spread and show that its sparsity tends to prevent outbreaks of rapidly spreading epidemics. Moreover, we define three typical behaviors according to the fractionfof links each individual shares outside its own department: residents, wanderers, and linkers. Linkers (f~ 50%) act as bridges in the network and have large betweenness centralities. Thus, a vaccination strategy targeting linkers efficiently prevents large outbreaks. As such a behavior may be spotteda prioriin the offices' organization or from surveys, without the full knowledge of the time-resolved contact network, this result may help the design of efficient, low-cost vaccination or social-distancing strategies.

Published
2015

31. Face-to-face interactions

Author

Alain Barrat, Ciro Cattuto, CPT - E5 Physique statistique et systèmes complexes, Centre de Physique Théorique - UMR 7332 (CPT), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Data Science Laboratory (ISI), ISI Foundation Institute for Scientific Interchange, and B. Gonçalves and N. Perra

Subjects
0303 health sciences, Computer science, Measure (physics), Wearable computer, Contact network, 01 natural sciences, Data science, 03 medical and health sciences, Face-to-face, Transmission (telecommunications), 0103 physical sciences, Social relationship, [PHYS.COND.CM-SM]Physics [physics]/Condensed Matter [cond-mat]/Statistical Mechanics [cond-mat.stat-mech], 010306 general physics, ComputingMilieux_MISCELLANEOUS, 030304 developmental biology
Abstract

Face-to-face interactions of humans play a crucial role in their social relationships as well as in the potential transmission of infectious diseases. Here we discuss recent research efforts and advances concerning the measure, analysis and modelling of such interactions measured using strategies ranging from surveys to decentralised infrastructures based on wearable sensors. We present a number of empirical characteristics of face-to-face interaction patterns and novel techniques aimed at uncovering mesoscopic structures in these patterns. We also mention recent modelling efforts and conclude with some open questions and challenges.

Published
2015

32. Mental health and social networks in early adolescence: A dynamic study of objectively-measured social interaction behaviors

Author

Emily J. Ozer, Ciro Cattuto, Mark C. Pachucki, Alain Barrat, CPT - E5 Physique statistique et systèmes complexes, Centre de Physique Théorique - UMR 7332 (CPT), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Centre de Physique Théorique - UMR 6207 (CPT), Centre National de la Recherche Scientifique (CNRS)-Université de Toulon (UTLN)-Université de Provence - Aix-Marseille 1-Université de la Méditerranée - Aix-Marseille 2, Data Science Laboratory (ISI), ISI Foundation Institute for Scientific Interchange, and Université de la Méditerranée - Aix-Marseille 2-Université de Provence - Aix-Marseille 1-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Male, Social inhibition, Health (social science), Adolescent, Social connectedness, media_common.quotation_subject, Psychology, Adolescent, California, Social networks, Developmental psychology, 03 medical and health sciences, Sex Factors, 0302 clinical medicine, History and Philosophy of Science, Self-esteem, Humans, Interpersonal Relations, 0501 psychology and cognitive sciences, Longitudinal Studies, 030212 general & internal medicine, Child, Students, Social influence, media_common, [SHS.SOCIO]Humanities and Social Sciences/Sociology, Depression, Early adolescence, Stochastic actor-based modeling, 4. Education, 05 social sciences, Social Support, Social environment, Mental health, Self Concept, Social relation, Interpersonal ties, Cross-Sectional Studies, Mental Health, Adolescent Behavior, Female, Psychology, Social psychology, 050104 developmental & child psychology
Abstract

International audience; How are social interaction dynamics associated with mental health during early stages of adolescence? The goal of this study is to objectively measure social interactions and evaluate the roles that multiple aspects of the social environment – such as physical activity and food choice – may jointly play in shaping the structure of children's relationships and their mental health. The data in this study are drawn from a longitudinal network-behavior study conducted in 2012 at a private K-8 school in an urban setting in California. We recruited a highly complete network sample of sixth-graders (n = 40, 91% of grade, mean age = 12.3), and examined how two measures of distressed mental health (self-esteem and depressive symptoms) are positionally distributed in an early adolescent interaction network. We ascertained how distressed mental health shapes the structure of relationships over a three-month period, adjusting for relevant dimensions of the social environment. Cross-sectional analyses of interaction networks revealed that self-esteem and depressive symptoms are differentially stratified by gender. Specifically, girls with more depressive symptoms have interactions consistent with social inhibition, while boys' interactions suggest robustness to depressive symptoms. Girls higher in self-esteem tended towards greater sociability. Longitudinal network behavior models indicate that gender similarity and perceived popularity are influential in the formation of social ties. Greater school connectedness predicts the development of self-esteem, though social ties contribute to more self-esteem improvement among students who identify as European-American. Cross-sectional evidence shows associations between distressed mental health and students' network peers. However, there is no evidence that connected students' mental health status becomes more similar in their over time because of their network interactions. These findings suggest that mental health during early adolescence may be less subject to mechanisms of social influence than network research in even slightly older adolescents currently indicates.

Published
2015

33. Is Web Content a Good Proxy for Real-Life Interaction?

Author

Kibanov, Mark, Atzmueller, Martin, Scholz, Christoph, Barrat, Alain, Cattuto, Ciro, Stumme, Gerd, Barrat, Alain, Knowledge and Data Engineering Group, University of Kassel, CPT - E5 Physique statistique et systèmes complexes, Centre de Physique Théorique - UMR 7332 (CPT), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Data Science Laboratory (ISI), and ISI Foundation Institute for Scientific Interchange

Subjects
[PHYS.COND.CM-SM] Physics [physics]/Condensed Matter [cond-mat]/Statistical Mechanics [cond-mat.stat-mech], [INFO.INFO-WB] Computer Science [cs]/Web, [INFO.INFO-WB]Computer Science [cs]/Web, [PHYS.COND.CM-SM]Physics [physics]/Condensed Matter [cond-mat]/Statistical Mechanics [cond-mat.stat-mech]
Abstract

International audience; —Today, many people spend a lot of time online. Their social interactions captured in online social networks are an important part of the overall personal social profile, in addition to interactions taking place offline. This paper investigates whether relations captured by online social networks can be used as a proxy for the relations in offline social networks, such as networks of human face-to-face (F2F) proximity and coauthorship networks. Particularly, the paper focuses on interactions of computer scientists in online settings (homepages, social networks profiles and connections) and offline settings (scientific collaboration, face-to-face communications during the conferences). We focus on quantitative studies and investigate the structural similarities and correlations of the induced networks; in addition, we analyze implications between networks. Finally, we provide a qualitative user analysis to find characteristics of good and bad proxies.

Published
2015

34. Contact Patterns in a High School: A Comparison between Data Collected Using Wearable Sensors, Contact Diaries and Friendship Surveys

Author

Mastrandrea, Rossana, Fournet, Julie, Barrat, Alain, Centre de Physique Théorique - UMR 7332 (CPT), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), CPT - E5 Physique statistique et systèmes complexes, Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Data Science Laboratory (ISI), ISI Foundation Institute for Scientific Interchange, ANR-12-MONU-0018,HarMS-flu,Approches multi-échelles pour la modélisation de la propagation de la grippe pilotée par les données.(2012), and European Project: 317532,EC:FP7:ICT,FP7-ICT-2011-8,MULTIPLEX(2012)

Subjects
FOS: Computer and information sciences, Male, Physics - Physics and Society, Adolescent, TRANSMISSION, INFECTIOUS-DISEASE, FOS: Physical sciences, lcsh:Medicine, Friends, Physics and Society (physics.soc-ph), Surveys and Questionnaires, Humans, [PHYS.COND]Physics [physics]/Condensed Matter [cond-mat], [PHYS.COND.CM-SM]Physics [physics]/Condensed Matter [cond-mat]/Statistical Mechanics [cond-mat.stat-mech], Students, lcsh:Science, Social and Information Networks (cs.SI), [PHYS]Physics [physics], Schools, Data Collection, lcsh:R, Social Support, Computer Science - Social and Information Networks, BIASES, humanities, Female, lcsh:Q, SPREAD, SOCIAL NETWORKS, Social Media, BEHAVIOR, Research Article
Abstract

Given their importance in shaping social networks and determining how information or transmissible diseases propagate in a population, interactions between individuals are the subject of many data collection efforts. To this aim, different methods are commonly used, ranging from diaries and surveys to decentralised infrastructures based on wearable sensors. These methods have each advantages and limitations but are rarely compared in a given setting. Moreover, as surveys targeting friendship relations might suffer less from memory biases than contact diaries, it is interesting to explore how actual contact patterns occurring in day-to-day life compare with friendship relations and with online social links. Here we make progresses in these directions by leveraging data collected in a French high school and concerning (i) face-to-face contacts measured by two concurrent methods, namely wearable sensors and contact diaries, (ii) self-reported friendship surveys, and (iii) online social links. We compare the resulting data sets and find that most short contacts are not reported in diaries while long contacts have a large reporting probability, and that the durations of contacts tend to be overestimated in the diaries. Moreover, measured contacts corresponding to reported friendship can have durations of any length but all long contacts do correspond to a reported friendship. On the contrary, online links that are not also reported in the friendship survey correspond to short face-to-face contacts, highlighting the difference of nature between reported friendships and online links. Diaries and surveys suffer moreover from a low sampling rate, as many students did not fill them, showing that the sensor-based platform had a higher acceptability. We also show that, despite the biases of diaries and surveys, the overall structure of the contact network, as quantified by the mixing patterns between classes, is correctly captured by both networks of self-reported contacts and of friendships, and we investigate the correlations between the number of neighbors of individuals in the three networks. Overall, diaries and surveys tend to yield a correct picture of the global structural organization of the contact network, albeit with much less links, and give access to a sort of backbone of the contact network corresponding to the strongest links, i.e., the contacts of longest cumulative durations.

Published
2015

35. Mitigation of infectious disease at school: targeted class closure vs school closure

Author

Alain Barrat, Ciro Cattuto, Valerio Gemmetto, Data Science Laboratory (ISI), ISI Foundation Institute for Scientific Interchange, CPT - E5 Physique statistique et systèmes complexes, Centre de Physique Théorique - UMR 7332 (CPT), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), and Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)

Subjects
IMPACT, Attack rate, Psychological intervention, CHILDREN, SOCIAL MIXING BEHAVIOR, Quantitative Biology - Quantitative Methods, Disease Outbreaks, 0302 clinical medicine, Economic cost, 030212 general & internal medicine, Child, Quantitative Methods (q-bio.QM), 0303 health sciences, education.field_of_study, Schools, 4. Education, Targeted interventions, 3. Good health, NETWORKS, Community-Acquired Infections, Infectious diseases, SPREAD, Psychology, Research Article, Risk, Physics - Physics and Society, medicine.medical_specialty, TRANSMISSION, Population, MODELS, FOS: Physical sciences, Physics and Society (physics.soc-ph), PANDEMIC INFLUENZA, 03 medical and health sciences, EPIDEMIC, Influenza, Human, medicine, School contact networks, Humans, Quantitative Biology - Populations and Evolution, education, CONTACT PATTERNS, 030304 developmental biology, Models, Statistical, Actuarial science, Public health, Populations and Evolution (q-bio.PE), Outbreak, Infectious disease (medical specialty), FOS: Biological sciences, Communicable Disease Control, Lower cost, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology
Abstract

Background School environments are thought to play an important role in the community spread of infectious diseases such as influenza because of the high mixing rates of school children. The closure of schools has therefore been proposed as an efficient mitigation strategy. Such measures come however with high associated social and economic costs, making alternative, less disruptive interventions highly desirable. The recent availability of high-resolution contact network data from school environments provides an opportunity to design models of micro-interventions and compare the outcomes of alternative mitigation measures. Methods and results We model mitigation measures that involve the targeted closure of school classes or grades based on readily available information such as the number of symptomatic infectious children in a class. We focus on the specific case of a primary school for which we have high-resolution data on the close-range interactions of children and teachers. We simulate the spread of an influenza-like illness in this population by using an SEIR model with asymptomatics, and compare the outcomes of different mitigation strategies. We find that targeted class closure affords strong mitigation effects: closing a class for a fixed period of time – equal to the sum of the average infectious and latent durations – whenever two infectious individuals are detected in that class decreases the attack rate by almost 70% and significantly decreases the probability of a severe outbreak. The closure of all classes of the same grade mitigates the spread almost as much as closing the whole school. Conclusions Our model of targeted class closure strategies based on readily available information on symptomatic subjects and on limited information on mixing patterns, such as the grade structure of the school, shows that these strategies might be almost as effective as whole-school closure, at a much lower cost. This may inform public health policies for the management and mitigation of influenza-like outbreaks in the community. Electronic supplementary material The online version of this article (doi:10.1186/s12879-014-0695-9) contains supplementary material, which is available to authorized users.

Published
2014

36. Assessing the impact of travel restrictions on international spread of the 2014 West African Ebola epidemic

Author

Marcelo F. C. Gomes, Ira M. Longini, Livio Bioglio, Dennis L. Chao, A Pastore y Piontti, M. E. Halloran, Loïc Rossi, Alessandro Vespignani, Vittoria Colizza, Chiara Poletto, Institut Pierre Louis d'Epidémiologie et de Santé Publique (iPLESP), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM), Northeastern University [Boston], ISI Foundation Institute for Scientific Interchange, Fred Hutchinson Cancer Research Center [Seattle] (FHCRC), University of Florida [Gainesville] (UF), and HAL UPMC, Gestionnaire

Subjects
Aircraft, Epidemiology, Global Health, Article, West africa, Disease Outbreaks, 03 medical and health sciences, 0302 clinical medicine, [SDV.MHEP.MI]Life Sciences [q-bio]/Human health and pathology/Infectious diseases, Virology, Global health, Humans, 030212 general & internal medicine, Socioeconomics, Africa, Western, Hemorrhagic Fever, Ebola, Travel, 030304 developmental biology, 0303 health sciences, Public Health, Environmental and Occupational Health, Outbreak, 3. Good health, West african, Geography, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, Africa, Ebola, [SDV.MHEP.MI] Life Sciences [q-bio]/Human health and pathology/Infectious diseases, Hemorrhagic Fever, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Western
Abstract

International audience; The quick spread of an Ebola outbreak in West Africa has led a number of countries and airline companies to issue travel bans to the affected areas. Considering data up to 31 Aug 2014, we assess the impact of the resulting traffic reductions with detailed numerical simulations of the international spread of the epidemic. Traffic reductions are shown to delay by only a few weeks the risk that the outbreak extends to new countries.

Published
2014

37. Data-driven spreading for the detection of weak ties

Author

Zhang, Qian, Karsai, Márton, Vespignani, Alessandro, Laboratory for the Modeling of Biological and Socio-technical Systems [Boston] (MoBS), Northeastern University [Boston], Dynamic Networks : Temporal and Structural Capture Approach (DANTE), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire de l'Informatique du Parallélisme (LIP), École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut Rhône-Alpin des systèmes complexes (IXXI), École normale supérieure de Lyon (ENS de Lyon)-Université Lumière - Lyon 2 (UL2)-Université Joseph Fourier - Grenoble 1 (UJF)-Université Jean Moulin - Lyon 3 (UJML), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Lumière - Lyon 2 (UL2)-Université Joseph Fourier - Grenoble 1 (UJF)-Université Jean Moulin - Lyon 3 (UJML), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Computational Epidemiology Laboratory (CERL), ISI Foundation Institute for Scientific Interchange, Institute for Quantitative Social Sciences, Harvard University, IMT-Lucca, École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), École normale supérieure - Lyon (ENS Lyon)-Université Lumière - Lyon 2 (UL2)-Université Joseph Fourier - Grenoble 1 (UJF)-Université Jean Moulin - Lyon 3 (UJML), and Harvard University [Cambridge]

Subjects
[INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], [INFO.INFO-NA]Computer Science [cs]/Numerical Analysis [cs.NA], [PHYS.PHYS.PHYS-DATA-AN]Physics [physics]/Physics [physics]/Data Analysis, Statistics and Probability [physics.data-an]
Abstract

International audience; Social ties of different strengths are assumed to maintain various features in the social network. Following the Granovetterian picture, weak ties are bridging between tightly connected communities, keeping the social structure globally connected, and they are assumed to be critical in disseminating information between far apart sides of the network. Their identification and control can provide effective ways to hinder and postpone outbreaks of spreading phenomena. In this work we propose a new method to infer the strength of social ties by using new data-driven simulation techniques. We return back to the original definition of Granovetter and qualify links by the importance they play during the propagation of information in the social structure. We apply data-driven spreading processes combined with a river-basin algorithmic method (see Fig.1) to identify links, which are the responsible to bring the information to large number of nodes. We investigate the correlations of the new importance measure with other conventional characteristics and identify their best combination through a percolation analysis to sophisticate further the assignment of social tie strengths. Finally we explore the role of the identified high importance links in control of globally spreading processes through data-driven SIR model simulations. These results point out that the size of infected population can be reduced considerably by weakening interactions through ties with high importance but zero overlap compared to strategies based on dyadic communications.

Published
2014

38. The representativeness of a European multi-center network for influenza-like-illness participatory surveillance

Author

Pietro Cantarelli, Vitor Faustino, Clément Turbelin, Marion Debin, Vittoria Colizza, Ken T. D. Eames, Daniela Paolotti, AnnaSara Carnahan, Daniel Lévy-Bruhl, Moa Rehn, Isabelle Bonmarin, Ronald Smallenburg, Ana O. Franco, John Edmunds, Thierry Blanchon, Chiara Poletto, Alessandra Micheletti, Carl Koppeschaar, Alessandro Vespignani, Alessandra Falchi, Thomas Hanslik, Università degli Studi di Milano [Milano] (UNIMI), Institut Pierre Louis d'Epidémiologie et de Santé Publique (iPLESP), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM), AP-HP, Service de Médecine Interne, Hôpital Ambroise Paré, F-92100, Boulogne Billancourt, France, Hôpital Ambroise Paré [AP-HP], Institut de Veille Sanitaire (INVS), ISI Foundation Institute for Scientific Interchange, Northeastern University [Boston], Harvard University [Cambridge], London School of Hygiene and Tropical Medicine (LSHTM), Aquisto-Inter BV, Instituto Gulbenkian de Ciência [Oeiras] (IGC), Fundação Calouste Gulbenkian, Public Health Agency of Sweden, Università degli Studi di Milano = University of Milan (UNIMI), and Harvard University

Subjects
Male, Influenza, Internet data collection, Participation bias, Representativeness, Selection bias, Surveillance, 020205 medical informatics, Health Status, 02 engineering and technology, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, Prevalence, Medicine, 030212 general & internal medicine, Child, media_common, Disease surveillance, education.field_of_study, Middle Aged, 3. Good health, Europe, Child, Preschool, Population Surveillance, Cohort, Female, Research Article, Adult, Adolescent, media_common.quotation_subject, Population, Representativeness heuristic, 03 medical and health sciences, Young Adult, Age Distribution, Environmental health, Influenza, Human, Humans, education, Aged, Internet, business.industry, Public Health, Environmental and Occupational Health, Infant, Newborn, Infant, Health indicator, Socioeconomic Factors, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Biostatistics, business
Abstract

Background The Internet is becoming more commonly used as a tool for disease surveillance. Similarly to other surveillance systems and to studies using online data collection, Internet-based surveillance will have biases in participation, affecting the generalizability of the results. Here we quantify the participation biases of Influenzanet, an ongoing European-wide network of Internet-based participatory surveillance systems for influenza-like-illness. Methods In 2011/2012 Influenzanet launched a standardized common framework for data collection applied to seven European countries. Influenzanet participants were compared to the general population of the participating countries to assess the representativeness of the sample in terms of a set of demographic, geographic, socio-economic and health indicators. Results More than 30,000 European residents registered to the system in the 2011/2012 season, and a subset of 25,481 participants were selected for this study. All age classes (10 years brackets) were represented in the cohort, including under 10 and over 70 years old. The Influenzanet population was not representative of the general population in terms of age distribution, underrepresenting the youngest and oldest age classes. The gender imbalance differed between countries. A counterbalance between gender-specific information-seeking behavior (more prominent in women) and Internet usage (with higher rates in male populations) may be at the origin of this difference. Once adjusted by demographic indicators, a similar propensity to commute was observed for each country, and the same top three transportation modes were used for six countries out of seven. Smokers were underrepresented in the majority of countries, as were individuals with diabetes; the representativeness of asthma prevalence and vaccination coverage for 65+ individuals in two successive seasons (2010/2011 and 2011/2012) varied between countries. Conclusions Existing demographic and national datasets allowed the quantification of the participation biases of a large cohort for influenza-like-illness surveillance in the general population. Significant differences were found between Influenzanet participants and the general population. The quantified biases need to be taken into account in the analysis of Influenzanet epidemiological studies and provide indications on populations groups that should be targeted in recruitment efforts. Electronic supplementary material The online version of this article (doi:10.1186/1471-2458-14-984) contains supplementary material, which is available to authorized users.

Published
2014

39. Contact patterns among high school students

Author

Julie Fournet, Alain Barrat, Centre de Physique Théorique - UMR 7332 (CPT), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), CPT - E5 Physique statistique et systèmes complexes, Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Data Science Laboratory (ISI), and ISI Foundation Institute for Scientific Interchange

Subjects
FOS: Computer and information sciences, Male, Gerontology, Epidemiology, lcsh:Medicine, Datasets as Topic, Wearable computer, Adolescents, Risk Factors, Medicine and Health Sciences, Longitudinal Studies, [PHYS.COND.CM-SM]Physics [physics]/Condensed Matter [cond-mat]/Statistical Mechanics [cond-mat.stat-mech], lcsh:Science, education.field_of_study, Schools, Multidisciplinary, Physics, 4. Education, Cosine similarity, Computer Science - Social and Information Networks, Social Networks, Physical Sciences, Interdisciplinary Physics, Female, France, Network Analysis, Research Article, Network analysis, Physics - Physics and Society, Computer and Information Sciences, Population, Stability (learning theory), FOS: Physical sciences, Physics and Society (physics.soc-ph), Biology, Social class, Communicable Diseases, Similarity (psychology), Humans, Social Behavior, Students, education, Social and Information Networks (cs.SI), lcsh:R, Models, Theoretical, Data science, Age Groups, Communicable disease transmission, People and Places, lcsh:Q, Population Groupings
Abstract

Face-to-face contacts between individuals contribute to shape social networks and play an important role in determining how infectious diseases can spread within a population. It is thus important to obtain accurate and reliable descriptions of human contact patterns occurring in various day-to-day life contexts. Recent technological advances and the development of wearable sensors able to sense proximity patterns have made it possible to gather data giving access to time-varying contact networks of individuals in specific environments. Here we present and analyze two such data sets describing with high temporal resolution the contact patterns of students in a high school. We define contact matrices describing the contact patterns between students of different classes and show the importance of the class structure. We take advantage of the fact that the two data sets were collected in the same setting during several days in two successive years to perform a longitudinal analysis on two very different timescales. We show the high stability of the contact patterns across days and across years: the statistical distributions of numbers and durations of contacts are the same in different periods, and we observe a very high similarity of the contact matrices measured in different days or different years. The rate of change of the contacts of each individual from one day to the next is also similar in different years. We discuss the interest of the present analysis and data sets for various fields, including in social sciences in order to better understand and model human behavior and interactions in different contexts, and in epidemiology in order to inform models describing the spread of infectious diseases and design targeted containment strategies., Comment: Supplementary Information at http://s3-eu-west-1.amazonaws.com/files.figshare.com/1677807/File_S1.pdf

Published
2014

40. Transmission centrality and the detection of week ties

Author

Zhang, Qian, Karsai, Márton, Vespignani, Alessandro, Laboratory for the Modeling of Biological and Socio-technical Systems [Boston] (MoBS), Northeastern University [Boston], Dynamic Networks : Temporal and Structural Capture Approach (DANTE), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire de l'Informatique du Parallélisme (LIP), École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut Rhône-Alpin des systèmes complexes (IXXI), École normale supérieure - Lyon (ENS Lyon)-Université Lumière - Lyon 2 (UL2)-Université Joseph Fourier - Grenoble 1 (UJF)-Université Jean Moulin - Lyon 3 (UJML), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Lumière - Lyon 2 (UL2)-Université Joseph Fourier - Grenoble 1 (UJF)-Université Jean Moulin - Lyon 3 (UJML), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Institute for Quantitative Social Sciences, Harvard University [Cambridge], Computational Epidemiology Laboratory (CERL), ISI Foundation Institute for Scientific Interchange, École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), École normale supérieure de Lyon (ENS de Lyon)-Université Lumière - Lyon 2 (UL2)-Université Joseph Fourier - Grenoble 1 (UJF)-Université Jean Moulin - Lyon 3 (UJML), and Harvard University

Subjects
[INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], [INFO.INFO-NA]Computer Science [cs]/Numerical Analysis [cs.NA], ComputingMilieux_MISCELLANEOUS, [PHYS.PHYS.PHYS-DATA-AN]Physics [physics]/Physics [physics]/Data Analysis, Statistics and Probability [physics.data-an]
Abstract

International audience

Published
2014

41. Effectiveness of 2012–2013 influenza vaccine against influenza-like illness in general population

Author

Debin, Marion, Colizza, Vittoria, Blanchon, Thierry, Hanslik, Thomas, Turbelin, Clement, Falchi, Alessandra, Institut Pierre Louis d'Epidémiologie et de Santé Publique (iPLESP), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM), ISI Foundation Institute for Scientific Interchange, AP-HP, Service de Médecine Interne, Hôpital Ambroise Paré, F-92100, Boulogne Billancourt, France, Hôpital Ambroise Paré [AP-HP], Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Service de Virologie, and Université Pascal Paoli (UPP)

Subjects
influenza, vaccine, GrippeNet, vaccine effectiveness, general population, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie
Abstract

International audience; Most of the methods used for estimating the influenza vaccine effectiveness (IVE) target the individuals who have an influenza-like illness (ILI) rather than virologically-proven influenza and access the healthcare system.The objective of this study was to estimate the 2012–2013 IVE in general French population, using a cohort of volunteers registered on GrippeNet.fr, an online surveillance system for ILI.The IVE estimations were obtained through a logistic regression, and analyses were also performed by focusing on at-risk population of severe influenza, and by varying inclusion period and ILI definition. Overall, 1996 individuals were included in the analyses. The corrected IVE was estimated to 49% (20 to 67) for the overall population, and 32% (0 to 58) for the at-risk population. Three covariables appeared with a significant effect on the occurrence of at least one ILI during the epidemic: the age (P = 0.045), the presence of a child in the household (P < 10−3), and the frequency of cold/flu (P < 10−3). Comparable results were found at epidemic peak time in the hypothesis of real-time feed of data.In this study, we proposed a novel, follow-up, web-based method to reveal seasonal vaccine effectiveness, which enables analysis in a portion of the population that is not tracked by the health care system in most VE studies.

Published
2014

42. Una geometrizzazione dello spazio degli accordi

Author

Bergomi, Mattia Giuseppe, Portaluri, Alessandro, Riccardo, Jadanza, Représentations musicales (Repmus), Sciences et Technologies de la Musique et du Son (STMS), Institut de Recherche et Coordination Acoustique/Musique (IRCAM)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche et Coordination Acoustique/Musique (IRCAM)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Università degli Studi di Milano [Milano] (UNIMI), Università degli studi di Torino (UNITO), ISI Foundation Institute for Scientific Interchange, Università degli Studi di Milano = University of Milan (UNIMI), and Università degli studi di Torino = University of Turin (UNITO)

Subjects
[MATH.MATH-GM]Mathematics [math]/General Mathematics [math.GM], [MATH]Mathematics [math]
Abstract

National audience; Si può trovare un’equazione per descrivereun brano musicale, o addiritturaper prevedere se sarà gradevoleall’ascolto? Sarebbe possibile modellareuna sinfonia con un certo numero diequazioni? La risposta a tutte queste domandeè facilmente intuibile da chiunqueaddetto e non addetto ai lavori ed è invariabilmenteun enfatico “no!”. Tuttavia sipossono riconoscere alcune strutture matematicheinsite in tutte le opere musicali,anche se non sono espresse attraversoequazioni. Il nostro viaggio attraverso“curiose iper-superfici singolari” iniziaproprio da qui. . .Musica e Matematica sono strettamente legate.Le connessioni tra questi due mondi sono ovunque:le corde di un’arpa vibrano ad una certafrequenza e hanno lunghezze specifiche regolateda rapporti ben precisi, le onde sonore possonoessere descritte da equazioni matematiche, la formadella cassa del violoncello è studiata per farlorisuonare con quel timbro caldo e suadente cheben conosciamo. La Matematica, in fondo, è illinguaggio che i fisici, i chimici e gli ingegneri(tra gli altri) usano per descrivere il mondo realee in alcuni casi anche per prevedere il futuro avolte lontano! Nel campo della Musica è un validostrumento per cercare di comprendere, seesistono, alcune strutture astratte sottostanti almetalinguaggio e aiutare così la Teoria Musicalenel suo intento di comunicazione e di educazioneall’ascolto. Chiunque abbia mai ascoltato il “Klavierstück”di Stockhausen, infatti, capisce beneche questo non è sempre facile da fare!

Published
2014

43. Time varying networks and the weakness of strong ties

Author

Nicola Perra, Márton Karsai, Alessandro Vespignani, Dynamic Networks : Temporal and Structural Capture Approach (DANTE), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire de l'Informatique du Parallélisme (LIP), École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut Rhône-Alpin des systèmes complexes (IXXI), École normale supérieure - Lyon (ENS Lyon)-Université Lumière - Lyon 2 (UL2)-Université Joseph Fourier - Grenoble 1 (UJF)-Université Jean Moulin - Lyon 3 (UJML), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Lumière - Lyon 2 (UL2)-Université Joseph Fourier - Grenoble 1 (UJF)-Université Jean Moulin - Lyon 3 (UJML), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Department of Biomedical Engineering and Computational Science (BECS), Aalto University School of Science and Technology [Aalto, Finland], Laboratory for the Modeling of Biological and Socio-technical Systems [Boston] (MoBS), Northeastern University [Boston], Computational Epidemiology Laboratory (CERL), ISI Foundation Institute for Scientific Interchange, Institute for Quantitative Social Sciences, Harvard University [Cambridge], École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), École normale supérieure de Lyon (ENS de Lyon)-Université Lumière - Lyon 2 (UL2)-Université Joseph Fourier - Grenoble 1 (UJF)-Université Jean Moulin - Lyon 3 (UJML), Harvard University, BECS, Northeastern University, Aalto-yliopisto, and Aalto University

Subjects
FOS: Computer and information sciences, QA75, Physics - Physics and Society, Theoretical computer science, Computer science, Process (engineering), FOS: Physical sciences, Variation (game tree), Physics and Society (physics.soc-ph), Community Networks, Article, Social Networking, [PHYS.PHYS.PHYS-COMP-PH]Physics [physics]/Physics [physics]/Computational Physics [physics.comp-ph], [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], Information system, Humans, QA, Network model, Social and Information Networks (cs.SI), ta113, Multidisciplinary, Information Dissemination, [PHYS.PHYS.PHYS-SOC-PH]Physics [physics]/Physics [physics]/Physics and Society [physics.soc-ph], Computer Science - Social and Information Networks, Strong ties, Models, Theoretical, [INFO.INFO-NA]Computer Science [cs]/Numerical Analysis [cs.NA], Interpersonal ties, Algorithms, Cell Phone, [PHYS.PHYS.PHYS-DATA-AN]Physics [physics]/Physics [physics]/Data Analysis, Statistics and Probability [physics.data-an]
Abstract

In most social and information systems the activity of agents generates rapidly evolving time-varying networks. The temporal variation in networks' connectivity patterns and the ongoing dynamic processes are usually coupled in ways that still challenge our mathematical or computational modelling. Here we analyse a mobile call dataset and find a simple statistical law that characterize the temporal evolution of users' egocentric networks. We encode this observation in a reinforcement process defining a time-varying network model that exhibits the emergence of strong and weak ties. We study the effect of time-varying and heterogeneous interactions on the classic rumour spreading model in both synthetic, and real-world networks. We observe that strong ties severely inhibit information diffusion by confining the spreading process among agents with recurrent communication patterns. This provides the counterintuitive evidence that strong ties may have a negative role in the spreading of information across networks., 22 pages, 15 figures

Published
2014
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44. The Scaling of Human Contacts in Reaction-Diffusion Processes on Heterogeneous Metapopulation Networks

Author

Tizzoni, Michele, Sun, Kaiyuan, Benusiglio, Diego, Karsai, Márton, Perra, Nicola, Computational Epidemiology Laboratory (CERL), ISI Foundation Institute for Scientific Interchange, Laboratory for the Modeling of Biological and Socio-technical Systems [Boston] (MoBS), Northeastern University [Boston], Dipartimento di Fisica, Politecnico di Torino = Polytechnic of Turin (Polito), Dynamic Networks : Temporal and Structural Capture Approach (DANTE), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire de l'Informatique du Parallélisme (LIP), École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut Rhône-Alpin des systèmes complexes (IXXI), École normale supérieure de Lyon (ENS de Lyon)-Université Lumière - Lyon 2 (UL2)-Université Joseph Fourier - Grenoble 1 (UJF)-Université Jean Moulin - Lyon 3 (UJML), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Lumière - Lyon 2 (UL2)-Université Joseph Fourier - Grenoble 1 (UJF)-Université Jean Moulin - Lyon 3 (UJML), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), and École normale supérieure - Lyon (ENS Lyon)-Université Lumière - Lyon 2 (UL2)-Université Joseph Fourier - Grenoble 1 (UJF)-Université Jean Moulin - Lyon 3 (UJML)

Subjects
Physics - Physics and Society, [PHYS.PHYS.PHYS-SOC-PH]Physics [physics]/Physics [physics]/Physics and Society [physics.soc-ph], Populations and Evolution (q-bio.PE), FOS: Physical sciences, complex networks, Physics and Society (physics.soc-ph), [PHYS.PHYS.PHYS-COMP-PH]Physics [physics]/Physics [physics]/Computational Physics [physics.comp-ph], epidemic spreading, [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], [INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA], social dynamics, FOS: Biological sciences, Quantitative Biology - Populations and Evolution, [PHYS.PHYS.PHYS-DATA-AN]Physics [physics]/Physics [physics]/Data Analysis, Statistics and Probability [physics.data-an]
Abstract

International audience; We present new empirical evidence, based on millions of interactions on Twitter, confirming that human contacts scale with population sizes. We integrate such observations into a reaction-diffusion metapopulation framework providing an analytical expression for the global invasion threshold of a contagion process. Remarkably, the scaling of human contacts is found to facilitate the spreading dynamics. Our results show that the scaling properties of human interactions can significantly affect dynamical processes mediated by human contacts such as the spread of diseases, and ideas.

Published
2014
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45. Controlling Contagion Processes in Time-Varying Networks

Author

Liu, Suyu, Perra, Nicola, Karsai, Márton, Vespignani, Alessandro, Department of Health Sciences, Northeastern University [Boston]-College of Computer and Information Sciences, Dynamic Networks : Temporal and Structural Capture Approach (DANTE), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire de l'Informatique du Parallélisme (LIP), École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut Rhône-Alpin des systèmes complexes (IXXI), École normale supérieure de Lyon (ENS de Lyon)-Université Lumière - Lyon 2 (UL2)-Université Joseph Fourier - Grenoble 1 (UJF)-Université Jean Moulin - Lyon 3 (UJML), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Lumière - Lyon 2 (UL2)-Université Joseph Fourier - Grenoble 1 (UJF)-Université Jean Moulin - Lyon 3 (UJML), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Computational Epidemiology Laboratory (CERL), ISI Foundation Institute for Scientific Interchange, Institute for Quantitative Social Sciences, Harvard University, École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), École normale supérieure - Lyon (ENS Lyon)-Université Lumière - Lyon 2 (UL2)-Université Joseph Fourier - Grenoble 1 (UJF)-Université Jean Moulin - Lyon 3 (UJML), and Harvard University [Cambridge]

Subjects
Social and Information Networks (cs.SI), FOS: Computer and information sciences, Physics - Physics and Society, FOS: Biological sciences, [PHYS.PHYS.PHYS-SOC-PH]Physics [physics]/Physics [physics]/Physics and Society [physics.soc-ph], Populations and Evolution (q-bio.PE), FOS: Physical sciences, Computer Science - Social and Information Networks, Physics and Society (physics.soc-ph), Quantitative Biology - Populations and Evolution, [INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI]
Abstract

International audience; The vast majority of strategies aimed at controlling contagion processes on networks considers the connectivity pattern of the system as either quenched or annealed. However, in the real world many networks are highly dynamical and evolve in time concurrently to the contagion process. Here, we derive an analytical framework for the study of control strategies specifically devised for time-varying networks. We consider the removal/immunization of individual nodes according the their activity in the network and develop a block variable mean-field approach that allows the derivation of the equations describing the evolution of the contagion process concurrently to the network dynamic. We derive the critical immunization threshold and assess the effectiveness of the control strategies. Finally, we validate the theoretical picture by simulating numerically the information spreading process and control strategies in both synthetic networks and a large-scale, real-world mobile telephone call dataset

Published
2013

46. Empirical temporal networks of face-to-face human interactions

Author

W. van den Broeck, Caterina Rizzo, Jean-François Pinton, Mariateresa Romano, Arturo Tozzi, Lorenzo Isella, Lucilla Ravà, Francesco Gesualdo, Vittoria Colizza, J. Stehlé, Ciro Cattuto, Alain Barrat, Elisabetta Pandolfi, CPT - E5 Physique statistique et systèmes complexes, Centre de Physique Théorique - UMR 7332 (CPT), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Centre de Physique Théorique - UMR 6207 (CPT), Université de la Méditerranée - Aix-Marseille 2-Université de Provence - Aix-Marseille 1-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Data Science Laboratory (ISI), ISI Foundation Institute for Scientific Interchange, Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Epidémiologie des maladies infectieuses et modélisation (ESIM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM), Epidemiology Unit, IRCCS Ospedale Pediatrico Bambino Gesù [Roma], Laboratoire de Physique de l'ENS Lyon (Phys-ENS), École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), National Centre for Epidemiology, Surveillance and Health Promotion, Istituto Superiore di Sanità (ISS), Centre National de la Recherche Scientifique (CNRS)-Université de Toulon (UTLN)-Université de Provence - Aix-Marseille 1-Université de la Méditerranée - Aix-Marseille 2, École normale supérieure - Lyon (ENS Lyon)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon, and Istituto Superiore di Sanità Rome, Rome, Italy

Subjects
Structure (mathematical logic), 0303 health sciences, Stylized fact, Computer science, Process (engineering), Scale (chemistry), General Physics and Astronomy, Wearable computer, Advertising, 01 natural sciences, Data science, Variety (cybernetics), 03 medical and health sciences, 0103 physical sciences, General Materials Science, Mobile technology, Physical and Theoretical Chemistry, [PHYS.COND.CM-SM]Physics [physics]/Condensed Matter [cond-mat]/Statistical Mechanics [cond-mat.stat-mech], 010306 general physics, Level of detail, 030304 developmental biology
Abstract

International audience; The ever increasing adoption of mobile technologies and ubiquitous services allows to sense human behavior at unprecedented level of details and scale. Wearable sensors, in particular, open up a new window on human mobility and proximity in a variety of indoor environments. Here we review stylized facts on the structural and dynamical properties of empirical networks of human face-to-face proximity, measured in three different real-world contexts: an academic conference, a hospital ward, and a museum exhibition. First, we discuss the structure of the aggregated contact networks, that project out the detailed ordering of contact events while preserving temporal heterogeneities in their weights. We show that the structural properties of aggregated networks highlight important differences and unexpected similarities across contexts, and discuss the additional complexity that arises from attributes that are typically associated with nodes in real-world interaction networks, such as role classes in hospitals. We then consider the empirical data at the finest level of detail, i.e., we consider time-dependent networks of face-to-face proximity between individuals. To gain insights on the effects that causal constraints have on spreading processes, we simulate the dynamics of a simple susceptible-infected model over the empirical time-resolved contact data. We show that the spreading pathways for the epidemic process are strongly affected by the temporal structure of the network data, and that the mere knowledge of static aggregated networks leads to erroneous conclusions about the transmission paths on the corresponding dynamical networks.

Published
2013

47. Estimating Potential Infection Transmission Routes in Hospital Wards Using Wearable Proximity Sensors

Author

Nagham Khanafer, Nicolas Voirin, Jean-François Pinton, Ciro Cattuto, Alain Barrat, Philippe Vanhems, Corinne Régis, Byeul-a Kim, Brigitte Comte, Service d'Hygiène, Epidémiologie et Prévention [Hôpital Edouard Herriot - HCL], Hôpital Edouard Herriot [CHU - HCL], Hospices Civils de Lyon (HCL)-Hospices Civils de Lyon (HCL), CPT - E5 Physique statistique et systèmes complexes, Centre de Physique Théorique - UMR 7332 (CPT), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Data Science Laboratory (ISI), ISI Foundation Institute for Scientific Interchange, Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Physique de l'ENS Lyon (Phys-ENS), École normale supérieure - Lyon (ENS Lyon)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon, Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Service de médecine gériatrique, Hospices Civils de Lyon (HCL)-Hôpital Edouard Herriot [CHU - HCL], Hospices Civils de Lyon (HCL), École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), and Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)

Subjects
Pediatrics, Wearable computer, lcsh:Medicine, 01 natural sciences, Quantitative Biology - Quantitative Methods, 010305 fluids & plasmas, law.invention, 0302 clinical medicine, law, [SDV.MHEP.MI]Life Sciences [q-bio]/Human health and pathology/Infectious diseases, Proximity sensor, Medicine, Infection transmission, 030212 general & internal medicine, Longitudinal Studies, lcsh:Science, Quantitative Methods (q-bio.QM), Geriatrics, 0303 health sciences, education.field_of_study, Cross Infection, Multidisciplinary, General Medicine, University hospital, 3. Good health, Transmission (mechanics), Medical emergency, General Agricultural and Biological Sciences, Research Article, medicine.medical_specialty, Physics - Physics and Society, Science, Population, FOS: Physical sciences, Physics and Society (physics.soc-ph), Communicable Diseases, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, 0103 physical sciences, Patients' Rooms, Disease Transmission, Infectious, Humans, 010306 general physics, education, 030304 developmental biology, business.industry, lcsh:R, Correction, medicine.disease, FOS: Biological sciences, Communicable disease transmission, Communicable Disease Control, lcsh:Q, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, business
Abstract

BACKGROUND: Contacts between patients, patients and health care workers (HCWs) and among HCWs represent one of the important routes of transmission of hospital-acquired infections (HAI). A detailed description and quantification of contacts in hospitals provides key information for HAIs epidemiology and for the design and validation of control measures. METHODS AND FINDINGS: We used wearable sensors to detect close-range interactions ("contacts") between individuals in the geriatric unit of a university hospital. Contact events were measured with a spatial resolution of about 1.5 meters and a temporal resolution of 20 seconds. The study included 46 HCWs and 29 patients and lasted for 4 days and 4 nights. 14,037 contacts were recorded overall, 94.1% of which during daytime. The number and duration of contacts varied between mornings, afternoons and nights, and contact matrices describing the mixing patterns between HCW and patients were built for each time period. Contact patterns were qualitatively similar from one day to the next. 38% of the contacts occurred between pairs of HCWs and 6 HCWs accounted for 42% of all the contacts including at least one patient, suggesting a population of individuals who could potentially act as super-spreaders. CONCLUSIONS: Wearable sensors represent a novel tool for the measurement of contact patterns in hospitals. The collected data can provide information on important aspects that impact the spreading patterns of infectious diseases, such as the strong heterogeneity of contact numbers and durations across individuals, the variability in the number of contacts during a day, and the fraction of repeated contacts across days. This variability is however associated with a marked statistical stability of contact and mixing patterns across days. Our results highlight the need for such measurement efforts in order to correctly inform mathematical models of HAIs and use them to inform the design and evaluation of prevention strategies.

Published
2013
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48. Gender homophily from spatial behavior in a primary school: a sociometric study

Author

Ciro Cattuto, Juliette Stehlé, François Charbonnier, Tristan Picard, Alain Barrat, Centre de Physique Théorique - UMR 7332 (CPT), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), École Nationale de la Statistique et de l'Administration Économique (ENSAE Paris), Data Science Laboratory (ISI), ISI Foundation Institute for Scientific Interchange, CPT - E5 Physique statistique et systèmes complexes, and Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS)

Subjects
FOS: Computer and information sciences, Physics - Physics and Society, Sociology and Political Science, FOS: Physical sciences, Physics and Society (physics.soc-ph), Homophily, Developmental psychology, 03 medical and health sciences, 5. Gender equality, 0501 psychology and cognitive sciences, 10. No inequality, General Psychology, 030304 developmental biology, Social and Information Networks (cs.SI), Sociometry, 0303 health sciences, 4. Education, [PHYS.PHYS.PHYS-SOC-PH]Physics [physics]/Physics [physics]/Physics and Society [physics.soc-ph], 05 social sciences, General Social Sciences, Computer Science - Social and Information Networks, School class, Strong ties, Interpersonal ties, Spatial behavior, Anthropology, Psychology, Gender preference, Statistical evidence, 050104 developmental & child psychology
Abstract

International audience; We investigate gender homophily in the spatial proximity of children (6 to 12 years old) in a French primary school, using time-resolved data on face-to-face proximity recorded by means of wearable sensors. For strong ties, i.e., for pairs of children who interact more than a defined threshold, we find statistical evidence of gender preference that increases with grade. For weak ties, conversely, gender homophily is negatively correlated with grade for girls, and positively correlated with grade for boys. This different evolution with grade of weak and strong ties exposes a contrasted picture of gender homophily.

Published
2013

49. On the Use of Human Mobility Proxies for Modeling Epidemics

Author

Vittoria Colizza, Marta C. González, Paolo Bajardi, Adeline Decuyper, Zbigniew Smoreda, Christian Schneider, Vincent D. Blondel, Guillaume Kon Kam King, Michele Tizzoni, Massachusetts Institute of Technology. Department of Civil and Environmental Engineering, Massachusetts Institute of Technology. Engineering Systems Division, Schneider, Christian M., Gonzalez, Marta C., ISI Foundation Institute for Scientific Interchange, Department of Veterinary Science, University of Turin, ICTEAM Institute, Université Catholique de Louvain = Catholic University of Louvain (UCL), Biostatistiques santé, Département biostatistiques et modélisation pour la santé et l'environnement [LBBE], Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Massachusetts Institute of Technology (MIT), Orange Labs [Issy les Moulineaux], France Télécom, Department of City and Regional Planning, University of California [Berkeley]-University of California, Sociology and Economics of Networks and Services Department, Orange Labs, Université Pierre et Marie Curie - Paris 6 - UFR de Médecine Pierre et Marie Curie (UPMC), Université Pierre et Marie Curie - Paris 6 (UPMC), ANR-12-MONU-0018,HarMS-flu,Approches multi-échelles pour la modélisation de la propagation de la grippe pilotée par les données.(2012), Università degli studi di Torino = University of Turin (UNITO), University of California [Berkeley] (UC Berkeley), University of California (UC)-University of California (UC)-University of California, UCL - SST/ICTM/INMA - Pôle en ingénierie mathématique, Università degli studi di Torino (UNITO), Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), and Modélisation et écotoxicologie prédictives

Subjects
Databases, Factual, STRATEGIES, Computer science, TRANSMISSION, [SDV]Life Sciences [q-bio], Automatic identification and data capture, Population Modeling, Transportation, PANDEMIC INFLUENZA, MEASLES, Communicable Diseases, Models, Biological, Proxy (climate), PREDICTABILITY, Cellular and Molecular Neuroscience, Modelling and Simulation, Influenza, Human, INFECTION, Genetics, Econometrics, Humans, Computer Simulation, Epidemics, lcsh:QH301-705.5, Molecular Biology, Ecology, Evolution, Behavior and Systematics, Simulation, [STAT.AP]Statistics [stat]/Applications [stat.AP], Ecology, Individual mobility, Biology and Life Sciences, Computational Biology, Spatial epidemiology, FORECAST, 3. Good health, Europe, HUMAN MOVEMENT, lcsh:Biology (General), Computational Theory and Mathematics, Mobile phone, Modeling and Simulation, Communicable disease transmission, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Unavailability, SPREAD, Infectious Disease Modeling, Epidemic model, Cell Phone, Research Article
Abstract

Human mobility is a key component of large-scale spatial-transmission models of infectious diseases. Correctly modeling and quantifying human mobility is critical for improving epidemic control, but may be hindered by data incompleteness or unavailability. Here we explore the opportunity of using proxies for individual mobility to describe commuting flows and predict the diffusion of an influenza-like-illness epidemic. We consider three European countries and the corresponding commuting networks at different resolution scales, obtained from (i) official census surveys, (ii) proxy mobility data extracted from mobile phone call records, and (iii) the radiation model calibrated with census data. Metapopulation models defined on these countries and integrating the different mobility layers are compared in terms of epidemic observables. We show that commuting networks from mobile phone data capture the empirical commuting patterns well, accounting for more than 87% of the total fluxes. The distributions of commuting fluxes per link from mobile phones and census sources are similar and highly correlated, however a systematic overestimation of commuting traffic in the mobile phone data is observed. This leads to epidemics that spread faster than on census commuting networks, once the mobile phone commuting network is considered in the epidemic model, however preserving to a high degree the order of infection of newly affected locations. Proxies' calibration affects the arrival times' agreement across different models, and the observed topological and traffic discrepancies among mobility sources alter the resulting epidemic invasion patterns. Results also suggest that proxies perform differently in approximating commuting patterns for disease spread at different resolution scales, with the radiation model showing higher accuracy than mobile phone data when the seed is central in the network, the opposite being observed for peripheral locations. Proxies should therefore be chosen in light of the desired accuracy for the epidemic situation under study., Author Summary The spatial dissemination of a directly transmitted infectious disease in a population is driven by population movements from one region to another allowing mixing and importation. Public health policy and planning may thus be more accurate if reliable descriptions of population movements can be considered in the epidemic evaluations. Next to census data, generally available in developed countries, alternative solutions can be found to describe population movements where official data is missing. These include mobility models, such as the radiation model, and the analysis of mobile phone activity records providing individual geo-temporal information. Here we explore to what extent mobility proxies, such as mobile phone data or mobility models, can effectively be used in epidemic models for influenza-like-illnesses and how they compare to official census data. By focusing on three European countries, we find that phone data matches the commuting patterns reported by census well but tends to overestimate the number of commuters, leading to a faster diffusion of simulated epidemics. The order of infection of newly infected locations is however well preserved, whereas the pattern of epidemic invasion is captured with higher accuracy by the radiation model for centrally seeded epidemics and by phone proxy for peripherally seeded epidemics.

Published
2013

50. The Role of Information Diffusion in the Evolution of Social Networks

Author

Bruno Gonçalves, Rossano Schifanella, Nicola Perra, Carlos Castillo, Filippo Menczer, Jacob Ratkiewicz, Lilian Weng, Alessandro Flammini, Francesco Bonchi, Department of Health Sciences, Northeastern University [Boston]-College of Computer and Information Sciences, Centre de Physique Théorique - UMR 6207 (CPT), Université de la Méditerranée - Aix-Marseille 2-Université de Provence - Aix-Marseille 1-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Centre de Physique Théorique - UMR 7332 (CPT), Aix Marseille Université (AMU)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Yahoo! Research [Barcelona], Università degli studi di Torino = University of Turin (UNITO), Data Science Laboratory (ISI), ISI Foundation Institute for Scientific Interchange, School of Informatics and Computing, Indiana University [Bloomington], Indiana University [Bloomington], Indiana University System-Indiana University System, Centre National de la Recherche Scientifique (CNRS)-Université de Toulon (UTLN)-Université de Provence - Aix-Marseille 1-Université de la Méditerranée - Aix-Marseille 2, Department of Computer Science, University of Turin, Italy, and University of Turin

Subjects
FOS: Computer and information sciences, Diffusion (acoustics), Physics - Physics and Society, J.4, Computer science, H.1, FOS: Physical sciences, Physics and Society (physics.soc-ph), 02 engineering and technology, World Wide Web, Computer Science - Computers and Society, [INFO.INFO-CY]Computer Science [cs]/Computers and Society [cs.CY], 020204 information systems, Computers and Society (cs.CY), H.1.2, 0202 electrical engineering, electronic engineering, information engineering, Social media, Set (psychology), Link (knot theory), Social and Information Networks (cs.SI), Social network, business.industry, [PHYS.PHYS.PHYS-SOC-PH]Physics [physics]/Physics [physics]/Physics and Society [physics.soc-ph], Computer Science - Social and Information Networks, Triadic closure, Key (cryptography), 020201 artificial intelligence & image processing, business, Mechanism (sociology)
Abstract

Every day millions of users are connected through online social networks, generating a rich trove of data that allows us to study the mechanisms behind human interactions. Triadic closure has been treated as the major mechanism for creating social links: if Alice follows Bob and Bob follows Charlie, Alice will follow Charlie. Here we present an analysis of longitudinal micro-blogging data, revealing a more nuanced view of the strategies employed by users when expanding their social circles. While the network structure affects the spread of information among users, the network is in turn shaped by this communication activity. This suggests a link creation mechanism whereby Alice is more likely to follow Charlie after seeing many messages by Charlie. We characterize users with a set of parameters associated with different link creation strategies, estimated by a Maximum-Likelihood approach. Triadic closure does have a strong effect on link formation, but shortcuts based on traffic are another key factor in interpreting network evolution. However, individual strategies for following other users are highly heterogeneous. Link creation behaviors can be summarized by classifying users in different categories with distinct structural and behavioral characteristics. Users who are popular, active, and influential tend to create traffic-based shortcuts, making the information diffusion process more efficient in the network., 9 pages, 10 figures, 2 tables

Published
2013

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