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This paper estimates the impact of violence on domestic and international migration in Mexico during 2005-2020. Unlike earlier studies, we use network analysis to quantify changes in the degree of interconnectedness among all municipalities in Mexico and with the US, breaking it down by origin-destination pairs. To identify the impact of violence on the changes of the migration network, we use instrumental variables. We exploit the exogenous variation in heroin street prices in the US, local prices of corn in Mexico, and differences in which regions received more Chinese immigrant communities during the 1930s, members of which introduced opium cultivation and drug smuggling networks in Mexico. These instruments help us identify the areas with a current presence of drug trafficking organizations and facing increases in violence. We show that municipalities that experience rises in homicide rates continue to attract immigrants within the country, but they face a larger rise in domestic emigration. The violence also discourages the return migration of Mexicans from the US and non-transit international emigration. Consistent with our findings, areas affected by violence also experience a long-term reduction in vehicle trips made in the highway network connecting them with the rest of the country.

Multiplex networks allow us to study a variety of complex systems where nodes connect to each other in multiple ways, for example friend, family, and co-worker relations in social networks. Link prediction is the branch of network analysis allowing us to forecast the future status of a network: which new connections are the most likely to appear in the future? In multiplex link prediction we also ask: of which type? Because this last question is unanswerable with classical link prediction, here we investigate the use of graph association rules to inform multiplex link prediction. We derive such rules by identifying all frequent patterns in a network via multiplex graph mining, and then score each unobserved link's likelihood by finding the occurrences of each rule in the original network. Association rules add new abilities to multiplex link prediction: to predict new node arrivals, to consider higher order structures with four or more nodes, and to be memory efficient. We improve over previous work by creating a framework that is also efficient in terms of runtime, which enables an increase in prediction performance. This increase in efficiency allows us to improve a case study on a signed multiplex network, showing how graph association rules can provide valuable insights to extend social balance theory., arXiv admin note: substantial text overlap with arXiv:2008.08351

The advent of social media has provided data and insights about how people relate to information and culture. While information is composed by bits and its fundamental building bricks are relatively well understood, the same cannot be said for culture. The fundamental cultural unit has been defined as a ``meme''. Memes are defined in literature as specific fundamental cultural traits, that are floating in their environment together. Just like genes carried by bodies, memes are carried by cultural manifestations like songs, buildings or pictures. Memes are studied in their competition for being successfully passed from one generation of minds to another, in different ways. In this paper we choose an empirical approach to the study of memes. We downloaded data about memes from a well-known website hosting hundreds of different memes and thousands of their implementations. From this data, we empirically describe the behavior of these memes. We statistically describe meme occurrences in our dataset and we delineate their fundamental traits, along with those traits that make them more or less apt to be successful.

Multiplex networks allow us to study a variety of complex systems where nodes connect to each other in multiple ways, for example friend, family, and co-worker relations in social networks. Link prediction is the branch of network analysis allowing us to forecast the future status of a network: which new connections are the most likely to appear in the future? In multiplex link prediction we also ask: of which type? Because this last question is unanswerable with classical link prediction, here we investigate the use of graph association rules to inform multiplex link prediction. We derive such rules by identifying all frequent patterns in a network via multiplex graph mining, and then score each unobserved link's likelihood by finding the occurrences of each rule in the original network. Association rules add new abilities to multiplex link prediction: to predict new node arrivals, to consider higher order structures with four or more nodes, and to be memory efficient. In our experiments, we show that, exploiting graph association rules, we are able to achieve a prediction performance close to an ideal ensemble classifier. Further, we perform a case study on a signed multiplex network, showing how graph association rules can provide valuable insights to extend social balance theory.

Network science is the field dedicated to the investigation and analysis of complex systems via their representations as networks. We normally model such networks as graphs: sets of nodes connected by sets of edges and a number of node and edge attributes. This deceptively simple object is the starting point of never-ending complexity, due to its ability to represent almost every facet of reality: chemical interactions, protein pathways inside cells, neural connections inside the brain, scientific collaborations, financial relations, citations in art history, just to name a few examples. If we hope to make sense of complex networks, we need to master a large analytic toolbox: graph and probability theory, linear algebra, statistical physics, machine learning, combinatorics, and more. This book aims at providing the first access to all these tools. It is intended as an "Atlas", because its interest is not in making you a specialist in using any of these techniques. Rather, after reading this book, you will have a general understanding about the existence and the mechanics of all these approaches. You can use such an understanding as the starting point of your own career in the field of network science. This has been, so far, an interdisciplinary endeavor. The founding fathers of this field come from many different backgrounds: mathematics, sociology, computer science, physics, history, digital humanities, and more. This Atlas is charting your path to be something different from all of that: a pure network scientist.

Multiplex networks allow us to study a variety of complex systems where nodes connect to each other in multiple ways, for example friend, family, and co-worker relations in social networks. Link prediction is the branch of network analysis allowing us to forecast the future status of a network: which new connections are the most likely to appear in the future? In multiplex link prediction we also ask: of which type? Because this last question is unanswerable with classical link prediction, here we investigate the use of graph association rules to inform multiplex link prediction. We derive such rules by identifying all frequent patterns in a network via multiplex graph mining, and then score each unobserved link's likelihood by finding the occurrences of each rule in the original network. Association rules add new abilities to multiplex link prediction: to predict new node arrivals, to consider higher order structures with four or more nodes, and to be memory efficient. In our experiments, we show that, exploiting graph association rules, we are able to achieve a prediction performance close to an ideal ensemble classifier. Further, we perform a case study on a signed multiplex network, showing how graph association rules can provide valuable insights to extend social balance theory., Accepted for publication in 15th International Conference on Web and Social Media (ICWSM) 2021

Estimating the distance covered by a propagation phenomenon on a network is an important task: it can help us estimating the infectiousness of a disease or the effectiveness of an online viral marketing campaign. However, so far the only way to make such an estimate relies on solving the optimal transportation problem, or by adapting graph signal processing techniques. Such solutions are either inefficient, because they require solving a complex optimization problem; or fragile, because they were not designed with this problem in mind. In this paper, we propose a new generalized Euclidean approach to estimate distances between weighted groups of nodes in a network. We do so by adapting the Mahalanobis distance, incorporating the graph's topology via the pseudoinverse of its Laplacian. In experiments we see that this measure returns intuitive distances which agree with the ones a human would estimate. We also show that the measure is able to recover the infection parameter in an epidemic model, or the activation threshold in a cascade model. We conclude by showing that the measure can be used in online social media settings to identify fast-spreading behaviors. Our measure is also less computationally expensive.

Oggetto di questo articolo è la Leggenda di sant’Antonio abate conservata nella terza unità codicologica del Mscr.Dresd.Ob.6, uno tra gli otto codici del XV sec. nella lingua del sì presenti nella Sächsische Landesbibliothek – Staats- und Universitätsbibliothek Dresden (SLUB). Avendo fornito alcune informazioni riguardanti l’intero manoscritto, ci si concentrerà sulla terza unità codicologica, descrivendone le principali caratteristiche formali e contenutistiche, collocando il testo ivi conservato nel più ampio contesto culturale della predicazione medievale in volgare. Sulla base di una rigorosa analisi del testo (analisi linguistica, ma anche analisi dei segni paragrafematici e della grafia), si tenterà di comprenderne l’uso e la provenienza. L’analisi si conclude con un paragrafo dedicato alla sintassi, rilevando come la Leggenda di sant’Antonio abate da un lato segua le norme sintattiche proprie dell’italiano antico e dall’altro sia fortemente influenzata dal modello neotestamentario e dalla sua funzione di supporto alla predicazione. Le conclusioni delle mie ricerche metteranno infine in luce il rapporto tra questo testo – con la sua punteggiatura e sintassi – e l’oralità propria del contesto omiletico.

In this document we describe the size of the Poblacion Flotante of Bogota (D.C.). The Poblacion Flotante is composed by people who live outside Bogota (D.C.), but who rely on the city for performing their job. We estimate the Poblacion Flotante impact relying on a new data source provided by telecommunications operators in Colombia, which enables us to estimate how many people commute daily from every municipality of Colombia to a specific area of Bogota (D.C.). We estimate that the size of the Poblacion Flotante could represent a 5.4% increase of Bogota (D.C.)'s population. During weekdays, the commuters tend to visit the city center more.

When the CoVid pandemic erupted in China, as serious and infectious as it proved to be, most people around the world did not consider it an emergency because they perceived China as being "far away" from them and thus, not a threat to their well-being. However, what people failed to realize is that despite the physical distance between themselves and China, the country is only a flight away from any place in the world. Although physical distance is much less of a meaningful barrier nowadays than it was in the past, our approach to measuring and predicting the global spread of phenomena such as information, relationships, cultural shifts and even disease is often based in the outdated perception that physical proximity is much more relevant than it actually is. This thesis work aims to prove that, instead of using physical distance to analyze and predict global spread, complex networks are a much more suitable technique because they illustrate the meaningful nodes and connections that determine the trajectory of the spread. To demonstrate this, CoVid 19 was used as a case study. In order to be able to compare the networks, a noise-corrected network backboning method was applied to reduce the largest network. For the modeling of the spread of the disease, the Euclidean The generalized method was applied in order to calculate the node-vector distance at subsequent time steps of the disease. As a result of this the study, two novel datasets corresponding to country-to-country proximity and country-to-country flights were generated, and the resulting Noise Corrected flight network was shown to more accurately model the spread of CoVid-19 in this particular instance.

Human action recognition has recently started to find its way into applications in different applications. Accordingly, human action recognition methods are becoming increasingly important in our daily life. They are used for different purposes such as automation, security, surveillance, health, smart home systems, and customer behaviour prediction, among others. Though have more systems with methods provides a rich pool of choices, it is important to well understand the performance of these systems and their success rates in recognizing the right activities in order to decide on the most appropriate system for the current application domain. This survey tackles this issue by analyzing and commenting on the available human action recognition systems and methods. IEEE; Association for Computing Machinery; IEEE Computer Society; ACM SIGKDD; IEEE TCDE; Springer; Elsevier

Il lavoro, in primo luogo, illustra la distinzione, affermatasi nel sistema multilaterale del commercio, tra caratteristiche del prodotto in quanto tale e caratteristiche dei metodi di produzione non collegate al prodotto, evidenziando le tensioni politiche che ne contraddistinguono il dibattito nell’Organizzazione mondiale del commercio (OMC). Quindi, si sofferma sul modo in cui l’Unione europea, attraverso la valorizzazione degli strumenti di informazione del consumatore, cerca di superare le divisioni della membership dell’OMC riguardo la possibilità di ricondurre le misure sui processi produttivi nel quadro ginevrino, contribuendo, così, secondo quanto indicato dallo stesso Preambolo dell’Accordo istitutivo dell’OMC, alla definizione di un sistema del commercio multilaterale ispirato al principio dello sviluppo sostenibile, e dunque sensibile agli importanti e immediati riflessi che le condizioni di produzione hanno sul rispetto dell’ambiente e dei diritti fondamentali, sul superamento delle condizioni di arretratezza dei Paesi in via di sviluppo, più in generale, sulla leale concorrenza negli scambi internazionali. In particolare, il saggio in oggetto pone l'accento sul fatto che gli schemi di etichettatura e certificazione ambientale e sociale, soprattutto quelli di carattere volontario, sono stati riconsiderati dall’Unione europea anche nel corso dell’elaborazione della sua strategia per lo sviluppo sostenibile: se appropriatamente congegnati e gestiti, infatti, essi sono suscettibili di rappresentare strumenti di mercato di importanza centrale per orientare i comportamenti delle imprese e dei consumatori verso modelli di investimento, produzione e consumo che risultino socialmente responsabili. Il lavoro giunge alla conclusione che l'Unione europea, con la valorizzazione degli strumenti di informazione del consumatore, non solo ha individuato un importante mezzo per perseguire la sua politica di sviluppo sostenibile, che, ormai, è un obiettivo di tutta la Comunità internazionale; l’Unione, così facendo, permette anche ai privati di superare il convincimento di essere sovrastati dalla realtà della globalizzazione, e di realizzare che, invece, la globalizzazione, se vissuta con gli adeguati strumenti di governance, fa acquisire maggior peso alle loro singole decisioni di carattere economico, permettendo ad aziende e consumatori, attraverso l’opportuno orientamento delle loro scelte economiche, di promuovere un miglioramento delle condizioni di lavoro e ambientali agendo nel contesto di un libero mercato.