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Integrated urban–rural development is important in rural vitalisation. Most publications have focused on the structural system of urban agglomerations but ignored urban–rural systems at the county level. To fill this research gap, a three-level construction method using network analysis is proposed for investigating the structure of the interconnection of rural and urban systems at the county level. Hierarchical systems among towns, between towns and villages as well as among rural settlements are recognised by the interconnection strength measured by multiple methods. The development strategy of each town is decided using cross discrimination based on its nodality and network centrality. The hierarchy between towns and villages is characterised by interconnection analysis between towns and villages and one village would be designated as the hinterland of the destination town with the highest frequency of commuting behaviour. The hierarchy among rural settlements is characterised by their interdependence using gravity model and the central settlements are identified according to the forms of settlement clusters with their respective three spatial patterns (i.e., single-centre, dual-core and linear). The city- and sub-centres of Daye, economic hinterland of each town and central settlements were identified to provide guidelines for the development of rural planning and vitalisation. Our case study demonstrates the potential of the proposed analysis framework for the guidance of the urban-rural system construction. However, further investigation and alternative data sources are needed to fully reflect the interactive intensity between towns and rural settlements.

The diffusion method is one of the main methods of community detection in complex networks. In this method, the use of the concept that diffusion within the nodes that are members of a community is faster than the diffusion of nodes that are not in the same community. In this way, the dense subgraph will detect the graph in the middle layer. The LPA algorithm, which mimics epidemic contagion by spreading labels, has attracted much attention in recent years as one of the most efficient algorithms in the subcategory of diffusion methods. This algorithm is one of the detection algorithms of most popular communities in recent years because of possessing some advantages including linear time order, the use of local information, and non-dependence on any parameter; however, due to the random behavior in LPA, there are some problems such as unstable and low quality resulting from larger monster communities. This algorithm is easily adaptable to attributed network. In this paper, it is supposed to propose a new version of the LPA algorithm for attributed graphs so that the detected communities solve the problems related to unstable and low quality in addition to possessing structural cohesiveness and attribute homogeneity. For this purpose, a weighted graph of the combination of node ​​attributes and topological structure is produced from an attributed graph for nodes which have edges with each other. Also, the centrality of each node will be calculated equal to the influence of each node using Laplacian centrality, and the steps of selecting the node are being enhanced for updating as well as the mechanism of updating based on the influence of nodes. The proposed method has been compared to other primary and new attributed graph clustering algorithms for real and artificial datasets. In accordance with the results of the experiments on the proposed algorithm without parameter adjusting for different networks of density and entropy criteria, the normalized mutual information indicates that the proposed method is more efficient and precise than other state-of-the-art attributed graph clustering methods.

This paper presents a critical examination of smart farming. I follow other critical analyses in recognizing the centrality of innovation processes in generating smart farming products, services, arrangements, and problematic outcomes. I subsequently use insights from critical human geography scholarship on the significance of understanding topological transformations to move beyond interpretations that identify only a narrow range of smart farming problems, such as a lack of coordination or limited uptake by farmers. Instead, I examine a broader set of challenges produced by smart farming developments. The overriding concern, I argue, is that smart farming unfolds via the production of numerous ‘misconfigured innovations.’ Using insights from literature on responsible research and innovation I then probe the stakes of looking beyond the misconfigured innovations of smart farming and discuss how new technologies might come to play a role in producing emancipatory smart farming. I pay attention to research on the ‘internet of people,’ which paints a stark new picture of social life generally, and in particular how rural life might be computed and calculated according to new conceptualizations of sociality and spatiality.

Exploring the complicated relationships underlying the clinical information is essential for the diagnosis and treatment of the Coronavirus Disease 2019 (COVID-19). Currently, few approaches are mature enough to show operational impact. Based on electronic medical records (EMRs) of 570 COVID-19 inpatients, we proposed an analysis model of diagnosis and treatment for COVID-19 based on the machine learning algorithms and complex networks. Introducing the medical information fusion, we constructed the heterogeneous information network to discover the complex relationships among the syndromes, symptoms, and medicines. We generated the numerical symptom (medicine) embeddings and divided them into seven communities (syndromes) using the combination of Skip-Gram model and Spectral Clustering (SC) algorithm. After analyzing the symptoms and medicine networks, we identified the key factors using six evaluation metrics of node centrality. The experimental results indicate that the proposed analysis model is capable of discovering the critical symptoms and symptom distribution for diagnosis; the key medicines and medicine combinations for treatment. Based on the latest COVID-19 clinical guidelines, this model could result in the higher accuracy results than the other representative clustering algorithms. Furthermore, the proposed model is able to provide tremendously valuable guidance and help the physicians to combat the COVID-19.

A Generalized Additive Game (GAG) (Cesari et al. in Int J Game Theory 46(4):919–939, 2017) is a Transferable Utility (TU) game (N, v), where each player in N is provided with an individual value, and the worth v(S) of a coalition $$S \subseteq N$$ is obtained as the sum of the individual values of players in another subset $$\mathcal {M}(S)\subseteq N$$ . Based on conditions on the map $$\mathcal {M}$$ (which associates to each coalition S a set of beneficial players $$\mathcal {M}(S)$$ not necessarily included in S), in this paper we characterize classes of GAGs that satisfy properties like monotonicity, superadditivity, (total) balancedness, PMAS-admissibility and supermodularity, for all nonnegative vectors of individual values. We also illustrate the application of such conditions on $$\mathcal {M}$$ over particular GAGs studied in the literature (e.g., glove games (Shapley and Shubik in Int Econ Rev 10:337–362, 1969), generalized airport games (Norde et al. in Eur J Oper Res 136(3):635–654, 2002), fixed tree games (Bjorndal et al. in Math Methods Oper Res 59(2):249–270, 2004), link-connection games (Moretti in Multi-agent systems and agreement technologies, vol 10767. Springer, Cham, 2008; Nagamochi et al. in Math Oper Res 22(1):146–164, 1997), simple minimum cost spanning tree games (Norde et al. in Eur J Oper Res 154(1):84–97, 2004; Tijs et al. in Eur J Oper Res 175(1):121–134, 2006) and graph coloring games (Deng et al. in Math Program 87(3):441–452, 2000; Hamers et al. in Math Program 145(1–2):509–529, 2014)).

Identifying important and influential nodes in complex networks is crucial in understanding, controlling, accelerating or terminating spreading processes for information, diseases, innovations, behaviors, and so on. Many existing centrality methods evaluate a node’s importance (or centrality) according to its neighbors, but the effects of its incident edges are always ignored or treated equally. However, in reality, edges always play different roles, which are usually measured by the edge centrality. Note that the centrality of a vertex is affected by the centralities of its incident edges, and conversely the centrality of an edge is determined by the centralities of its two endpoints. In this paper, we present a novel way to evaluate the centrality for both nodes and edges simultaneously by constructing a mutually updated iterative framework. Furthermore, we will prove that the node centralities obtained by this framework are actually the principal eigenvector of the signless-laplacian matrix of the input network, thus we call this new node centrality method as signless-laplacian eigenvector centrality method. We test it on several classical data sets and all produce satisfying results. It is expected to have a promising applications in the future.

The rising field of Topological Data Analysis (TDA) provides a new approach to learning from data through persistence diagrams, which are topological signatures that quantify topological properties of data in a comparable manner. For point clouds, these diagrams are often derived from the Vietoris-Rips filtration—based on the metric equipped on the data—which allows one to deduce topological patterns such as components and cycles of the underlying space. In metric trees these diagrams often fail to capture other crucial topological properties, such as the present leaves and multifurcations. Prior methods and results for persistent homology attempting to overcome this issue mainly target Rips graphs, which are often unfavorable in case of non-uniform density across our point cloud. We therefore introduce a new theoretical foundation for learning a wider variety of topological patterns through any given graph. Given particular powerful functions defining persistence diagrams to summarize topological patterns, including the normalized centrality or eccentricity, we prove a new stability result, explicitly bounding the bottleneck distance between the true and empirical diagrams for metric trees. This bound is tight if the metric distortion obtained through the graph and its maximal edge-weight are small. Through a case study of gene expression data, we demonstrate that our newly introduced diagrams provide novel quality measures and insights into cell trajectory inference., Pattern Recognition Letters, 147, ISSN:0167-8655, ISSN:1872-7344

Resumo As novas centralidades passaram a ter um papel mais destacado na malha urbana brasileira desde a década de 2000. A cidade de Pau dos Ferros, localizada no estado do Rio Grande do Norte, destacou-se ao expandir sua influência regional no período 2003-2014. O objetivo deste texto é entender como o gasto público fortaleceu a centralidade regional dessa cidade. A hipótese discutida é de que o gasto público é um elemento fundamental para garantir a integração urbano-regional de cidades intermediárias localizadas nas áreas não metropolitanas do Nordeste brasileiro. Conclui-se que, dentre os gastos públicos, aqueles direcionados ao ensino superior foram decisivos para que o município de Pau dos Ferros atingisse maior relevância regional. Abstract The new centralities started to play a more prominent role in the Brazilian urban network in the 2000s. The city of Pau dos Ferros, located in the state of Rio Grande do Norte, stood out by expanding its regional influence between 2003 and 2014. The purpose of this text is to understand how public spending has strengthened the regional centrality of Pau dos Ferros. The hypothesis discussed here is that public spending is a fundamental element to guarantee the urban--regional integration of intermediate cities located in the non-metropolitan areas of Northeastern Brazil. The conclusion is that, among public expenditures, those directed to higher education were decisive for the municipality of Pau dos Ferros to achieve greater regional relevance.

This article is part of the Economic Geography studies and deals with aspects related to intermodal logistics with particular reference to maritime transport of containers. Ports are historically one of the key infrastructures for the Italian economy. In recent decades, along with many industrial sectors that were once driving forces for the national economy, due to the lack of adequate strategic planning, it has failed to keep pace with the rapid changes imposed on all sectors involved in the process of globalization of the economic world, losing ground and competitiveness with respect to many competing realities, both in Northern Europe and in the Mediterranean basin itself [1]. Most of the other states of Mediterranean Europe are in similar conditions, but Italy is probably the one that has lost the most ground. In this situation of strong economic and social imbalance, the covid-19 epidemic has hit Italy before and dramatically and if it had not turned into a pandemic, hitting the rest of Europe and the planet and forcing the European institutions to launch, for the first time, concrete support measures for all, would probably have represented a fatal shock for the Italian economy. By elaborating the information on statistical data and projections available in numerous international publications on the transport economy and more specifically on logistics, taking into account the reasons for the successes and failures of similar realities to the Italian one, the study offers food for thought on what to do immediately and in the future, taking advantage of the unique and unrepeatable opportunity constituted by the economic initiatives for the post-pandemic recovery, to remedy the lost ground by returning an adequate role to our world of maritime and port transport which can also be a driving force for the territory behind, avoiding ending up on the edge of the evolved world.

Aim of the study A centre-periphery structure reflects spatial inequalities in the level of economic development of countries and regions. Most often, it provides a simplified picture of spatial distribution of income or spatial accessibility. In this study the authors try to identify the relationship between the level of centrality/peripherality of an area and selected features of the personal car market, using the case of Poland. Method In order to identify the relationship between the level of centrality/peripherality of an area and selected features of the peresonal car market, the correlation and regression analysis has been used. As a dependent variable the index of centrality/peripherality consisting of population and enterprise income has been calculated for all Polish communes (gminas). The features of the car market (independent variables) are: 1) car ownership (number of cars per 1000 inhabitants), 2) sales of new cars, 3) the import of second-hand cars, and 4) the average age of personal cars. Result The research confirmed a positive correlation between the index of centrality/peripherality (and hence the central character of the commune) and the sales of new automobiles, and a negative correlation with the average age of cars. There is no correlation between the level of centrality/peripherality of an area and the indicators of car ownership and the import of second-hand vehicles.

This paper contributes to understanding the international trade of plastic scrap. This is done by structuring trade data in networks, and calculating network centrality indexes for countries engaged in plastic scrap trade, as well as for sub-types of plastic polymers (Ethylene, PVC, and Styrene). Network measurements allow insights on plastic scrap flows, as they identify important countries which concentrate material flows at scale, useful for technology dissemination, recycling infrastructure deployment, as well for standard-setting and negotiations. Network measurements based on 2018 data show the key role of EU and North American countries in the global plastic scrap trade network, with strong links to a few Asian countries. Developing regions such as South America, Africa, and Eurasia displayed discreet roles in the networks analyzed, indicating the need for more research on underreporting or other factors behind this apparent underrepresentation.

PurposeApplications of social network analysis (SNA) are evidently popular amongst scholars for mapping stakeholder and other relational networks in improving the sustainability of construction activities and the resulting built environment. Nevertheless, the literature reveals a lack of thorough understanding of optimal SNA applications in this field. Therefore, this paper aims to convey a comprehensive critical review of past applications of SNA in this field.Design/methodology/approach95 relevant journal papers were initially identified from the “Web of Science” database and a bibliometric analysis was carried out using the “VOS Viewer” software. The subsequent in-depth review of the SNA methods, focussed on 24 specifically relevant papers selected from these aforesaid 95 papers.FindingsA significant growth of publications in this field was identified after 2014, especially related to topics on stakeholder management. “Journal of Cleaner Production”, “International Journal of Project Management” and “Sustainability” were identified as the most productive sources in this field, with the majority of publications from China. Interviews and questionnaires were the popular data collection methods while SNA “Centrality” measures were utilised in over 70% of the studies. Furthermore, potential areas were noted, to improve the mapping and thereby provide useful information to managers who could influence relevant networks and consequentially better sustainability outcomes, including those enhanced by collaborative networks.Originality/valueCloser collaboration has been found to help enhance sustainability in construction and built environment, hence attracting research interest amongst scholars on how best to enable this. SNA is established as a significant methodological approach to analysing interrelationships and collaborative potential in general. In a pioneering application here, this paper initiates the drawing together of findings from relevant literature to provide useful insights for future researchers to comprehensively identify, compare and contrast the applications of SNA techniques in construction and built environment management from a sustainability viewpoint.

In recent years, the prevalence of chronic diseases such as type 2 diabetes mellitus (T2DM) has increased, bringing a heavy burden to healthcare systems. While regular monitoring of patients is expensive and impractical, understanding chronic disease progressions and identifying patients at risk of developing comorbidities are crucial. This research used a real-world administrative claim dataset of T2DM to develop an ensemble of innovative patient network and machine learning approach for disease prediction. The healthcare data of 1,028 T2DM patients and 1,028 non-T2DM patients are extracted from the de-identified data to predict the risk of T2DM. The proposed model is based on the ‘patient network’, which represents the underlying relationships among health conditions for a group of patients diagnosed with the same disease using the graph theory. Besides patients’ socio-demographic and behaviour characteristics, the attributes of the ‘patient network’ (e.g., centrality measure) discover patients’ latent features, which are effective in risk prediction. We apply eight machine learning models (Logistic Regression, K-Nearest Neighbours, Support Vector Machine, Naive Bayes, Decision Tree, Random Forest, XGBoost and Artificial Neural Network) to the extracted features to predict the chronic disease risk. The extensive experiments show that the proposed framework with machine learning classifiers performance with the Area Under Curve (AUC) ranged from 0.79 to 0.91. The Random Forest model outperformed the other models; whereas, eigenvector centrality and closeness centrality of the network and patient age are the most important features for the model. The outstanding performance of our model provides promising potential applications in healthcare services. Also, we provide strong evidence that the extracted latent features are essential in the disease risk prediction. The proposed approach offers vital insight into chronic disease risk prediction that could benefit healthcare service providers and their stakeholders.

Going against the mainstream popularity of high-rise housing developments dominating the Turkish construction market in the late twentieth century, Haluk Baysal and Melih Birsel collaboratively built the Istanbul Yesilkoy Housing Community project in 1973 using the principles of modern architecture. Although the modern lifestyle dominant in the design of this housing project did not emerge from the principles of life inherent to Turkish domestic architecture, this study examines how this project could remain resistant to the destructive force of a possible earthquake in a location where the land value is higher than many other districts of Istanbul. This study’s research question is whether or not the survival of this residential housing community could be attributed to residential satisfaction. The study’s research method was a residential satisfaction survey and factor analyses. The results showed that the continued existence of the residential community in the midst of the urban transformation movement is because the community satisfied its residents’ needs for safety, centrality, and neighbourliness.

In Named Data Networking (NDN) architecture, data can be obtained from multiple content sources (i.e., producers or caching nodes) with multiple paths, making the traditional end-to-end (i.e., TCP/IP) congestion control scheme invalid. In addition, the NDN multi-path discovery and management are still an open issue as the dynamic network topology changes. In this article, we propose a multi-path congestion control mechanism, named MPCC, which includes two major components, i.e., multi-path discovery and multi-path congestion control. Particularly, for multi-path discovery, we first devise a path tag to uniquely mark each sub-path in the forwarding process, and then propose a tag-aware forwarding strategy to discover and manage sub-paths. For multi-path selection and congestion control, we first integrate the metrics of packet loss, bandwidth, round trip time, and path centrality, for path assessment, based on which, we then leverage the Upper Confidence Bound (UCB) algorithm to select sub-paths in order to maximize the network throughput. In addition, for selected sub-paths, we have devised a sub-path window adaptation algorithm to avoid multi-path congestions. At last, we implement MPCC in ndnSIM and conduct extensive experiments for performance evaluation. Our results demonstrate that MPCC can discover all sub-paths in real-time for the multi-path scenario, and can effectively avoid multi-path congestions with improving throughput and reducing transmission time.

We define a bridge node to be a node whose neighbor nodes are sparsely connected to each other and are likely to be part of different components if the node is removed from the network. We propose a computationally light neighborhood-based bridge node centrality (NBNC) tuple that could be used to identify the bridge nodes of a network as well as rank the nodes in a network on the basis of their topological position to function as bridge nodes. The NBNC tuple for a node is asynchronously computed on the basis of the neighborhood graph of the node that comprises of the neighbors of the node as vertices and the links connecting the neighbors as edges. The NBNC tuple for a node has three entries: the number of components in the neighborhood graph of the node, the algebraic connectivity ratio of the neighborhood graph of the node and the number of neighbors of the node. We analyze a suite of 60 complex real-world networks and evaluate the computational lightness, effectiveness, efficiency/accuracy and uniqueness of the NBNC tuple vis-a-vis the existing bridgeness related centrality metrics and the Louvain community detection algorithm.

Bayesian statistical analysis is steadily growing in popularity and use. Choosing priors is an integral part of Bayesian inference. While there exist extensive normative recommendations for prior setting, little is known about how priors are chosen in practice. We conducted a survey (N = 50) and interviews (N = 9) where we used interactive visualizations to elicit prior distributions from researchers experienced with Bayesian statistics and asked them for rationales for those priors. We found that participants' experience and philosophy influence how much and what information they are willing to incorporate into their priors, manifesting as different levels of informativeness and skepticism. We also identified three broad strategies participants use to set their priors: centrality matching, interval matching, and visual probability mass allocation. We discovered that participants' understanding of the notion of "weakly informative priors"---a commonly-recommended normative approach to prior setting---manifests very differently across participants. Our results have implications both for how to develop prior setting recommendations and how to design tools to elicit priors in Bayesian analysis.

OSMnx is a Python package for downloading OpenStreetMap street network data and then constructing it into NetworkX graphs. OSMnx can simplify and correct the network’s topology automatically to ensure that nodes actually exclusively represent intersections and dead-ends. Once the network is constructed and corrected, OSMnx can calculate shortest paths from one node to another. It can also calculate various network measures relevant to urban design and transportation as well as statistical physics, including intersection density, average intersection degree, edge density, average street segment length, circuity, clustering coefficients, betweenness centrality, closeness centrality, PageRank, and many more. Its built-in visualization capabilities leverage matplotlib to easily plot routes, one-way streets, dead-ends, high/low connectivity intersections, and figure-ground diagrams of street networks and urban form.

Influence maximization is an important research problem in the field of network science because of its business value. It requires the strategic selection of seed nodes called “influential nodes,” such that information originating from these nodes can reach numerous nodes in the network. Many real-world networks, such as transportation, communication, and social networks, are weighted networks. Influence maximization in a weighted network is more challenging compared to that in an unweighted network. Many methods, such as weighted degree rank, weighted h-index, weighted betweenness, and weighted VoteRank techniques, have been used to order the nodes based on their spreading capabilities in weighted networks. The VoteRank method is a popular method for finding influential nodes in an unweighted network using the idea of a voting scheme. Recently, the WVoteRank method was proposed to find the seed nodes; it extends the idea of the VoteRank method by considering the edge weights. This method considers only 1-hop neighbors to calculate the voting score of every node. In this study, we propose an improved WVoteRank method based on an extended neighborhood concept, which takes the 1-hop neighbors as well as 2-hop neighbors into account for the voting process to decide influential nodes in a weighted network. We also extend our proposed approach to unweighted networks. We compare the performance of the proposed improved WVoteRank method against the popular centrality measures, weighted degree, weighted closeness, weighted betweenness, weighted h-index, and weighted VoteRank on several real-life and synthetic datasets of diverse sizes and properties. We utilize the widely used stochastic susceptible–infected–recovered information diffusion model to calculate the infection scale, the final infected scale as a function of time, and the average distance between spreaders. The simulation results reveal that the proposed method, improved WVoteRank, considerably outperforms the other methods described above, including the recent WVoteRank.

For studying the mechanism of flight conflict in approach of the intersecting runway, this paper applies the case study, scenario construction, and complex network, analyzes the operational risks of the intersecting runway, and researches the general rule of flight conflict. We constructed a network model of scenario evolution of flight conflict with selecting Beijing Daxing International Airport as the research object, which included 169 nodes and 263 edges. It proposed path evolution and verified the effectiveness of this network. We analyzed the degree centrality, median centrality, and closeness centrality of the network, and the results showed that the comprehensive value of 5 nodes is high, including go-around (V2), conflict resolution (C22), the warning light of aft cargo dJor was extinguished (F12), suspend subsequent take-off operations (F5), and keeping visual flying (C26). The results show that this method provides a new research way for the control strategy of chain breakage and the mechanism of scenario evolution of flight conflict.

Introduction Individual assessment disregards the team aspect of clinical work. Team assessment collapses the individual into the group. Neither is sufficient for medical education, where measures need to attend to the individual while also accounting for interactions with others. Valid and reliable measures of interdependence are critical within medical education given the collaborative manner in which patient care is provided. Medical education currently lacks a consistent approach to measuring the performance between individuals working together as part of larger healthcare team. This review's objective was to identify existing approaches to measuring this interdependence. Methods Following Arksey & O'Malley's methodology, we conducted a scoping review in 2018, and updated it to 2020. A search strategy involving five databases located >12,000 citations. At least two reviewers independently screened titles and abstracts, screened full-texts (n=161), and performed data extraction on twenty-seven included articles. Interviews were also conducted with key informants to check if any literature was missing and assess that our interpretations made sense. Results Eighteen of the twenty-seven articles were empirical; nine conceptual with an empirical illustration. Eighteen were quantitative; nine used mixed methods. The articles spanned five disciplines and various application contexts, from online learning to sports performance. Only two of the included articles were from the field of Medical Education. The articles conceptualized interdependence of a group, using theoretical constructs such as collaboration synergy; of a network, using constructs such as degree centrality; and of a dyad, using constructs such as synchrony. Both descriptive (e.g., social network analysis) and inferential (e.g., multi-level modelling) approaches were described. Conclusion Efforts to measure interdependence are scarce and scattered across disciplines. Multiple theoretical concepts and inconsistent terminology may be limiting programmatic work. This review motivates the need for further study of measurement techniques, particularly those combining multiple approaches, to capture interdependence in medical education.

Ranking firms in an inter-firm transaction network is a crucial task for identifying key players in an industry, thereby explaining the agglomeration of economic activities and assisting with competitor identification. To the best of our knowledge, despite the advantages of network-based approaches in market analysis, few studies have employed network analysis tools to rank firms. However, these studies failed to capture the characteristics of inter-firm transaction networks (i.e., evolving over time, having multiple edges between nodes, among others). In this study, we propose a new ranking method, FirmRank, that identifies key players based on centrality metrics in network analysis, leveraging inter-firm transactions to discern the characteristics of an inter-firm transaction network. Our proposed ranking method is evaluated using real-world datasets from a corporate information database, and the evaluation results demonstrate the superiority of our method over well-known ranking methods—PageRank and age-based PageRank.

With an advent of social networks, spamming has posted the most important serious issues among the users. These are termed as influential users who spread the spam messages in the community which has created the social and psychological impact on the users. Hence the identification of such influential nodes has become the most important research challenge. The paper proposes with a method to (1) detect a community using community algorithms with the Laplacian Transition Matrix that is the popular hashtag (2) to find the Influential nodes or users in the Community using Intelligent centrality measure's (3) The implementation of machine learning algorithm to classify the intensity of users.The extensive experimentations has been carried out using the COVID-19 datasets with the different machine learning algorithms. The methodologies SVM and PCA provide the accuracy of 98.6 than the linear regression for using the new centrality measures and the other scores like NMI, RMS, are found for the methods. As a result finding out the Influential nodes will help us find the Spammy and genuine accounts easily.

Android, the most popular mobile operating system, has attracted millions of users around the world. Meanwhile, the number of new Android malware instances has grown exponentially in recent years. On the one hand, existing Android malware detection systems have shown that distilling the program semantics into a graph representation and detecting malicious programs by conducting graph matching are able to achieve high accuracy on detecting Android malware. However, these traditional graph-based approaches always perform expensive program analysis and suffer from low scalability on malware detection. On the other hand, because of the high scalability of social network analysis, it has been applied to complete large-scale malware detection. However, the social-network-analysis-based method only considers simple semantic information (i.e., centrality) for achieving market-wide mobile malware scanning, which may limit the detection effectiveness when benign apps show some similar behaviors as malware. In this article, we aim to combine the high accuracy of traditional graph-based method with the high scalability of social-network-analysis--based method for Android malware detection. Instead of using traditional heavyweight static analysis, we treat function call graphs of apps as complex social networks and apply social-network--based centrality analysis to unearth the central nodes within call graphs. After obtaining the central nodes, the average intimacies between sensitive API calls and central nodes are computed to represent the semantic features of the graphs. We implement our approach in a tool called IntDroid and evaluate it on a dataset of 3,988 benign samples and 4,265 malicious samples. Experimental results show that IntDroid is capable of detecting Android malware with an F-measure of 97.1% while maintaining a True-positive Rate of 99.1%. Although the scalability is not as fast as a social-network-analysis--based method (i.e., MalScan ), compared to a traditional graph-based method, IntDroid is more than six times faster than MaMaDroid . Moreover, in a corpus of apps collected from GooglePlay market, IntDroid is able to identify 28 zero-day malware that can evade detection of existing tools, one of which has been downloaded and installed by more than ten million users. This app has also been flagged as malware by six anti-virus scanners in VirusTotal, one of which is Symantec Mobile Insight .

This paper aims to present an evaluation of targeted attack sequences based on a new set of power traffic centrality measures according to the vulnerability prediction measure (VPM) approach. A framework for evaluation of attack proposed in previous work is applied using three fault strategies: remove most central element first (RMCEF), iterated most central element first, and iterated electrical most damaging element first (IMDEF). For attacks on nodes, the reliability of the IMDEF strategy is confirmed, as it was the most predictive in terms of the VPM. Nevertheless, in attacks performed on links, the IMDEF does not always represents the most harmful attack. Regarding the new centralities, the Katz centrality consistently presented high values of VPM for attacks on nodes and links, with results that are comparable to degree and eigenvector centralities. In terms of execution times, the percolation centrality is not recommended, as it presented the highest execution times. The RMCEF strategy with degree, eigenvector and Katz centralities is a good estimation of the most harmful attack sequences on nodes and links with a shorter execution time than the IMDEF.

The core hypothesis of this paper is that neuropsychoanalysis provides a new paradigm for artificial general intelligence (AGI). The AGI agenda could be greatly advanced if it were grounded in affective neuroscience and neuropsychoanalysis rather than cognitive science. Research in AGI has so far remained too cortical-centric; that is, it has privileged the activities of the cerebral cortex, the outermost part of our brain, and the main cognitive functions. Neuropsychoanalysis and affective neuroscience, on the other hand, affirm the centrality of emotions and affects—i.e., the subcortical area that represents the deepest and most ancient part of the brain in psychic life. The aim of this paper is to define some general design principles of an AGI system based on the brain/mind relationship model formulated in the works of Mark Solms and Jaak Panksepp. In particular, the paper analyzes Panksepp’s seven effective systems and how they can be embedded into an AGI system through Judea Pearl’s causal analysis. In the conclusions, the author explains why building a sub-cortical AGI is the best way to solve the problem of AI control. This paper is intended to be an original contribution to the discussion on AGI by elaborating positive arguments in favor of it.

Pathogenicity-related studies are of great importance in understanding the pathogenesis of complex diseases and improving the level of clinical medicine. This work proposed a bioinformatics scheme to analyze cancer-related gene mutations, and try to figure out potential genes associated with diseases from the protein domain-domain interaction network. Herein, five measures of the principle of centrality lethality had been adopted to implement potential correlation analysis, and prioritize the significance of genes. This method was further applied to KEGG pathway analysis by taking the malignant melanoma as an example. The experimental results show that 25 domains can be found, and 18 of them have high potential to be pathogenically important related to malignant melanoma. Finally, a web-based tool, named Human Cancer Related Domain Interaction Network Analyzer, is developed for potential pathogenic genes prioritization for 26 types of human cancers, and the analysis results can be visualized and downloaded online.

Myocardial infarction (MI) is a type of coronary heart disease, which refers to the ischemic necrosis of the heart muscle. A large number of studies have discussed the mechanism of MI from the perspective of competing endogenous RNA (ceRNA) network. However, the mechanisms underlying the function of lncRNAs in MI have still not been explained in an explicit manner. Therefore, we constructed a scale-free lncRNA-associated ceRNA network to identify some crucial lncRNAs in MI. Results showed that the given disease genes for MI were involved in the network, the degrees of which were significantly larger than the other nodes of the network. For measuring the network centrality, we then constructed a hub subnetwork. The miRNAs and mRNAs in the hub subnetwork have been validated to function in MI-related biological function. In addition, we identified 2 MI-related functional modules from the lncRNA-associated ceRNA network, which suggested that lncRNA exerted function in local network. Enrichment analysis showed that these functional modules corresponded to some similar and different pathways related to cardiovascular disease. More importantly, 3 MI-related crucial lncRNAs, CTD-3092A11.2, RP5-821D11.7 and CTC-523E23.1 were detected as potential biomarkers, which may be involved in MI-related biological progresses. Our study identified 20 functional lncRNAs based on ceRNA network analysis, which may provide novel diagnosis and therapeutic targets for MI from the ceRNA network perspective.

Influence maximization is an optimization problem in the area of social graph analysis, which asks to choose a subset of k individuals to maximize the number of influenced nodes at the end of the diffusion process. As individuals within a community have frequent contact and are more likely to influence each other, community-based influence maximization has attracted considerable attentions. However, this kind of works ignores the role of overlapping nodes in community structure, resulting in performance degradation in seeds selection. In addition, many existing community-based algorithms identify the final seeds only from the selected important communities, or they need to leverage the weights between local spread and global spread of a node. It is difficult to set suitable scales for important communities or to determine the weights for different spread. In this paper, we propose a novel influence maximization approach based on overlapping community structure and node coverage gain. Firstly, social graphs are partitioned into different overlapping communities by the algorithm of node location analysis in topological potential field. Secondly, a node coverage gain sensitive centrality measure is put up to evaluate the influence of each node locally within its belonging communities, which avoids the problem of local spread and global spread. Finally, seed nodes are directly selected by combining the detected community structure with the pre-designed strategy, without important communities identification. The comprehensive experiments under both the Uniform Independent Cascade model and the Weighted Independent Cascade model demonstrate that our proposed approach can achieve competitive influence spread, outperforming state-of-the-art works. Furthermore, our proposed approach exhibits stable performance on graphs with different scales and various structural characteristics.

Background Some proposed methods for identifying essential proteins have better results by using biological information. Gene expression data is generally used to identify essential proteins. However, gene expression data is prone to fluctuations, which may affect the accuracy of essential protein identification. Therefore, we propose an essential protein identification method based on gene expression and the PPI network data to calculate the similarity of "active" and "inactive" state of gene expression in a cluster of the PPI network. Our experiments show that the method can improve the accuracy in predicting essential proteins. Results In this paper, we propose a new measure named JDC, which is based on the PPI network data and gene expression data. The JDC method offers a dynamic threshold method to binarize gene expression data. After that, it combines the degree centrality and Jaccard similarity index to calculate the JDC score for each protein in the PPI network. We benchmark the JDC method on four organisms respectively, and evaluate our method by using ROC analysis, modular analysis, jackknife analysis, overlapping analysis, top analysis, and accuracy analysis. The results show that the performance of JDC is better than DC, IC, EC, SC, BC, CC, NC, PeC, and WDC. We compare JDC with both NF-PIN and TS-PIN methods, which predict essential proteins through active PPI networks constructed from dynamic gene expression. Conclusions We demonstrate that the new centrality measure, JDC, is more efficient than state-of-the-art prediction methods with same input. The main ideas behind JDC are as follows: (1) Essential proteins are generally densely connected clusters in the PPI network. (2) Binarizing gene expression data can screen out fluctuations in gene expression profiles. (3) The essentiality of the protein depends on the similarity of "active" and "inactive" state of gene expression in a cluster of the PPI network.

Knowing the ultimate oil production in wells is a crucial point for reservoir planning and management to anticipate value for money. Commercial reservoir simulators are able to predict production curves with high confidence, but repetitive tasks in a few cases may spend a precious time of staff as well as require a large computational effort. Although artificial intelligence (AI) is providing an alternative path to the usual workflow, many commercial simulators lack robust AI algorithms. This work introduces a methodology based on a multilayer perceptron (MLP) neural network to predict the final cumulative oil production of a reservoir at vertical wells that cross hydraulic flow units (HFUs), which are volumes endowed with good flow attributes. Each well location is attached to special spots previously determined from clustering and calculation of maximum closeness centrality points (MaxCs) within a class of HFUs. The database is divided into training, validation, and testing sets organized after processing the UNISIM-I-D synthetic model, representative of the Namorado Field, Campos Basin, Brazil. The key rationale of this paper is to use the feature of MaxCs of being drivers for well placement as knowledge base to learn the production mechanisms of the oilfield. The outcomes are presented from two perspectives: an original MLP and its post-processed version. Both are compared with reservoir simulations carried out in CMG Imex$$^{\copyright }$$ © and achieve reasonable agreement. The performance is measured by root-mean-squared error (RMSE) and mean absolute scaled error (MASE) both in original and post-processed versions. We show that average RMSE and MASE values near 0.07 and 14.00, respectively, are achieved without post-processing. With post-processing, gains of up to 43% are reported for the integral oil volume.

We develop an empirical electrical-based framework to compare between centrality measures as to judge their ability to predict the vulnerability of smart grids and their elements under various attacks. The centrality measures considered are based on a weighted graph adjacency matrix representing the real power flows. The vulnerability is measured by the post-attack unsatisfied load (UL), which is determined through steady-state simulation using the MatPower v6.0. We introduce a generalized vulnerability curve as a plot of measures of electrical damage (e.g., the UL), versus physical damage. We consider various measures of physical damage such as the Fraction of Elements (FOE) removed and sums of centrality scores of elements removed. The area under the vulnerability curve (denoted as VPM) is shown to be a logical, reliable, and consistent indicator of the predictive power of a centrality measure. The VPM is simulated for several attacks including the Remove Most Central Elements First (RMCEF) attack. We show that degree centrality is the most predictive, when compared to eigenvector and betweenness centralities. Moreover, the degree-based RMCEF attack is the worst among the RMCEF and 5400 random attacks. The FOE-degree centrality VPM is the most predictive as well as the most computationally efficient.

Ischemic stroke (IS) is one of the major causes of death and disability worldwide. However, the specific mechanism of gene interplay and the biological function in IS are not clear. Therefore, more research into IS is necessary. Dataset GSE110993 including 20 ischemic stroke (IS) and 20 control specimens are used to establish both groups and the raw RNA‐seq data were analysed. Weighted gene co‐expression network analysis (WGCNA) was used to screen the key micro‐RNA modules. The centrality of key genes were determined by module membership (mm) and gene significance (GS). The key pathways were identified by enrichment analysis with Kyoto Protocol Gene and Genome Encyclopedia (KEGG), and the key genes were validated by protein‐protein interactions network. Result: Upon investigation, 1185 up‐ and down‐regulated genes were gathered and distributed into three modules in response to their degree of correlation to clinical traits of IS, among which the turquoise module show a trait‐correlation of 0.77. The top 140 genes were further identified by GS and MM. KEGG analysis showed two pathways may evolve in the progress of IS. Discussion: CXCL12 and EIF2a may be important biomarkers for the accurate diagnosis and treatment in IS.

Cruise shipping is one of the fastest-growing sectors in the maritime industry. This is due to the growing demand of the sector and the extension of capacity and cruise service coverage. This study uses network measures and random network models to analyse the structure, characteristics and connectivity of the cruise shipping network in Asia and Australasia. An analysis of 200 cruise voyages operated by 12 international cruise lines between 139 ports across 20 countries in the Asian and Australasian regions has found that the cruise shipping network is a scale-free network. This means the degree distribution follows a power law. A few ports play a central role in connecting ports in the network. However, their importance in the network varies depending on which measure of centrality is used, i.e. the degree, betweenness, closeness and eigenvector centrality. The establishment of a link between two ports is influenced by their connections with a third one or triangle relationship.

The essential protein detection on protein–protein interaction (PPI) network can not only promote the research of life science, but also have important applications in disease diagnosis and drug target cell identifying. A large number of computation-based essential protein detection algorithms have been presented recently. Most of those methods detect the essential proteins according to the centrality measures of the nodes in PPI networks. Those centrality-based essential protein detection methods only consider the topological information of the PPI networks and neglect the biological features of the proteins which are crucial in recognizing the essential proteins. This paper presents a random walk-based method named EPD-RW to identify essential proteins by integrating network topology and biological information extracted from GO (gene ontology) data, gene expression profiles, domain information and phylogenetic profile. EPD-RW uses both the topological structure of the PPI and biological information of the proteins to guide the random walk for computing their essentialness. An iterative method is presented to efficiently integrate the topological and biological features at each step of the random walk. We test our method EDP-RW by experiments on yeast PPI datasets. We also compare the test results of EDP-RW with those of other methods. The experimental results demonstrate that EPD-RW can achieve the best performance among all the methods tested. The biological illustration of the results shows that our random walk-based method effectively increases the accuracy of essential proteins detecting results, and the biological features of the proteins can greatly enhance the performance of essential protein detecting.

In recent years, data scheduling in satellite networks has drawn much attention. The nodes in the satellite network change dynamically, and there is no continuous connectivity between nodes. Due to the intermittent connection between satellites, data transmission is based on a storage-carry-forward mode. In such a network, it will inevitably result in transmission delay. It is challenging to schedule a large amount of observation data with delay guarantees in resource-limited satellite networks. In this paper, we propose a downlink aware data scheduling algorithm based on a decomposition strategy of rolling horizon mechanism. The data flows with deadlines are allocated to ground stations orderly through feasible routes during each sliding horizon. Considering the issues of time-varying node centrality and downlink capacity, a link metric is provided to solve the problem of downlink contact conflict. Then a heuristic routing algorithm is exploited to design inter-satellite link contact. Further simulation results demonstrate the effectiveness of the proposed algorithms in network profit improvement while ensuring the transmission delay demand.

Industry is an important pillar of the national economy, and industrial projects are the most complex and difficult to manage and control in the construction industry; thus, the resource scheduling control of industrial projects is one of the core issues for industrial construction projects. The performance rate of the contract time periods of previous industrial construction projects has been very low. In scheduling control based on case-based reasoning (CBR), the goal is to implement preventive measures by referring to existing scheduling control cases and control the scheduling of resources through reasoning on emergency measures to prevent scheduling control deviations. In this paper, the rough set approach is used to represent the case feature information in a case reasoning model for industrial project scheduling control, attribute reduction is used to determine the weights of the feature attributes in the rough set representation, and the similarity between cases is calculated for case retrieval. The accuracy of the rough-set-based similarity calculation is verified through matrix similarity calculations and a visual analysis of the all closeness centrality and weighted all degree centrality of the corresponding complex network; thus, similar cases of industrial project scheduling control are identified. To verify the applicability and effectiveness of the proposed methodology, a typical coal chemical general contract project case is carried out. The rough set comprehensive similarity results were 0.733, 0.621, 0.536, 0.614, 0.559, 0.950, 0.708, 0.546, 0.733, 0.664, 0.526, and 0.743, and the matrix similarity results were 0.417, 0.583, 0.417, 0.417, 0.417, 0.833, 0.417, 0.500, 0.417, 0.500, 0.333, and 0.500. The results showed that the case retrieval accuracy of traditional matrix similarity is not as high as the rough set comprehensive similarity, so X 6 is the most similar case to the target case Y. Case retrieval results indicate that the proposed methodology can provide a good similar case selection strategy with project managers, and the final required preventive measures for the target case can be found. Based on the identified similar cases, preventive measures for scheduling control are formulated to effectively prevent scheduling deviations of industrial projects.

The problem of identifying the top-k influential node is still an open and deeply felt issue. The development of a stable and efficient algorithm to deal with such identification is still a challenging research hot spot. Although conventional centrality-based and greedy-based methods show high performance, they are not very efficient when dealing with large-scale social networks. Recently, algorithms based on swarm intelligence are applied to solve the problems mentioned above, and the existing researches show that such algorithms can obtain the optimal global solution. In particular, the discrete bat algorithm (DBA) has been proved to have excellent performance, but the evolution mechanism based on a random selection strategy leads to the optimal solution's instability. To solve this problem, in this paper, we propose a clique-DBA algorithm. The proposed algorithm is based on the clique partition of a network and enhances the initial DBA algorithm's stability. The experimental results show that the proposed clique-DBA algorithm converges to a determined local influence estimation (LIE) value in each run, eliminating the phenomenon of large fluctuation of LIE fitness value generated by the original DBA algorithm. Finally, the simulated results achieved under the independent cascade model show that the clique-DBA algorithm has a comparable performance of influence spreading compared with the algorithms proposed in the state of the art.

The frequent occurrences of cascading failures in power grids have been receiving continuous attention in recent years. An urgent task for us is to assess the vulnerability of power grids against various kinds of attacks, which trigger cascading failures. In this brief, we formulate a cost-constrained hybrid attack in power grids, where both nodes and links are targeted with a limited total attack cost. Based on the consequence and cost of removing a component (node or link), we propose an attack centrality metric for components, which can be either local or global depending on the depth of cascading failures. We further propose a greedy hybrid attack and another optimal hybrid attack by applying the attack centrality. Simulation results on IEEE bus test data demonstrate that the optimal attack is more efficient than the greedy one. Furthermore, we find, counterintuitively, that the local centrality based attack algorithms perform better than the global centrality based ones when attack cost is a concern. Our work can help in the robustness optimization of power systems by revealing the worst-case attack vulnerability and most vulnerable components.

This paper presents an Agent-Based Model that determines the minimum cost strategy for rural electrification delivery systems and associated decentralized generation focusing entirely on renewable resources. The model creates generalized scenarios examining stakeholders' preferences for renewable resources to deploy and preferences between centralized and decentralized generation. Literature suggests that low demand, topographical challenges and distance from the grid are the main drivers for decentralized generation. This work shows that available energy potential is an important driver as well by introducing the concepts of stress (the percentage of renewable energy potential taken up by demand) and centrality (the ratio between size of resources and average distance to demand) and demonstrating that these parameters influence the minimum cost strategy despite distance and demand. At very low stress levels the lowest cost networks result from emphasizing decentralized generation. At stress levels above 2%, least cost delivery systems result from emphasizing grid extension of clusters created around resources with high centrality and the decentralized mix is high due to the geographical limitations of renewables. Results show that in every case, while deploying renewable energy for rural electrification, a mixture of grid extension and decentralized generation coexist for universal electrification in the least cost strategies.

The formation of public opinions is a complex phenomenon that revolves around the aggregation of individuals’ beliefs. To accurately capture this phenomenon, one needs to build links from individualistic experiences to personal beliefs, which evolve in a social space through information exchange and belief revision. Despite many efforts to model opinion dynamics, the role of personal experiences and beliefs has often been overlooked. In this paper, we address this issue and propose an agent-based model in a social network. We explicitly model belief acquisition as a learning process from experiences that take the form of local data sets. Agents interact through a social network and update their beliefs based on how accurately the belief reflects experiences. Through iterations of interactions, the agents are able to arrive at a unified belief. We then focus on the accuracy of the personal beliefs during their evolution and the impact of the social network structure. On a micro-level, we investigate positional attributes such as the centrality of nodes that affect belief accuracy. On a macro-level, we investigate structural features that affect the overall performance. We then investigate a method to intervene in opinion formation through expert agents. Experiments are performed on real-world and synthetic data sets, which validate a number of important structural insights.

One of the most harmful consequences of social media is the rise of cyberbullying, which tends to be more sinister than traditional bullying, given that online records typically live on the Internet for quite a long time and are hard to control. In this article, we present a three-phase algorithm, called BullyNet, for detecting cyberbullies on Twitter social network. We exploit bullying tendencies by proposing a robust method for constructing a cyberbullying signed network (SN). We analyze tweets to determine their relation to cyberbullying while considering the context in which the tweets exist in order to optimize their bullying score. We also propose a centrality measure to detect cyberbullies from a cyberbullying SN and show that it outperforms other existing measures. We experiment on a data set of 5.6 million tweets, and our results show that the proposed approach can detect cyberbullies with high accuracy while being scalable with respect to the number of tweets.

Many authoritarian regimes have taken to censoring internet access in order to stop the spread of misinformation, restrict citizens from discussing certain topics, and prevent mobilization, among other reasons. There are several theories about the effectiveness of censorship. Some suggest that censorship will effectively limit the flow of information, whereas others predict that a backlash will form, resulting in ultimately more discussion about the topic. In this work, we analyze the role of communities and gatekeepers during multiple internet outages in Venezuela in January 2019. First, we measure how critical information (e.g., entities and hashtags) spreads during outages focusing on information recurrence and burstiness within and across language and location communities. We discover that information bursts tend to cross both language and location community boundaries rather than being limited to a single community during several outages. Then we identify users who play central roles and propose a novel method to detect gatekeepers—users who prevent critical information from spreading across communities during outages. We show that bilingual and English-speaking users play more central roles compared to Spanish-speaking users, but users inside and outside Venezuela have similar distribution of centrality. Finally, we measure the differences in social network structure before and after each outage event and discuss its effect on how information spreads. We find that with each outage event social connections tend to get less connected with higher mean shortest path, indicating that the effect of censorship makes it harder for information to spread.

In the last 20 years, terrorism has led to hundreds of thousands of deaths and massive economic, political, and humanitarian crises in several regions of the world. Using real-world data on attacks occurred in Afghanistan and Iraq from 2001 to 2018, we propose the use of temporal meta-graphs and deep learning to forecast future terrorist targets. Focusing on three event dimensions, i.e., employed weapons, deployed tactics and chosen targets, meta-graphs map the connections among temporally close attacks, capturing their operational similarities and dependencies. From these temporal meta-graphs, we derive 2-day-based time series that measure the centrality of each feature within each dimension over time. Formulating the problem in the context of the strategic behavior of terrorist actors, these multivariate temporal sequences are then utilized to learn what target types are at the highest risk of being chosen. The paper makes two contributions. First, it demonstrates that engineering the feature space via temporal meta-graphs produces richer knowledge than shallow time-series that only rely on frequency of feature occurrences. Second, the performed experiments reveal that bi-directional LSTM networks achieve superior forecasting performance compared to other algorithms, calling for future research aiming at fully discovering the potential of artificial intelligence to counter terrorist violence., Comment: 19 pages, 18 figures

Searching for key nodes in social networks and clustering communities are indispensable components in community detection methods. With the wide application demand of detecting community networks, more and more algorithms have been proposed. Laplacian centrality peaks clustering (LPC) is an efficient and simple algorithm which is proposed on the basis of density peaks clustering (DPC) to identify clusters without parameters and prior knowledge. Before LPC is widely applied in community detection algorithms, some shortcomings should be addressed. Firstly, LPC fails to search for key nodes in networks accurately because of the similarity calculation method. Secondly, it takes too much time for LPC to calculate the Laplacian centrality of each point. To address these issues, a community detection algorithm based on Quasi-Laplacian centrality peaks clustering (CD-QLPC) is proposed after studying the advantages of Quasi-Laplacian centrality which can replace density or Laplacian centrality to characterize the importance of nodes in networks. Quasi-Laplacian centrality is obtained by the degree of each node directly, which needs less time than Laplacian centrality. In addition, a trust-based function is utilized to obtain the similarity accurately. Moreover, a new modularity-based merging strategy is adopted to identify the optimal number of communities adaptively. Experimental results show that CD-QLPC outperforms many state-of-the-art methods on both real-world networks and synthetic networks.

The integration of the social network concepts and IoT has led to the creation of a new concept called Social IoT, wherein objects or things can interact and provide services to one another. Efficient and distributed service navigation in such systems is essential due to the potentially huge number of services that are provided. This paper proposes a Distributed Learning Automata-based algorithm called DLA_N, which aims at finding service-providers (i.e., objects) with high popularity or influence. The main idea is that an object can use its friends or friends of its friends to search for the desired service provider. Taking into account the SIoT’s graph properties (i.e., the topology of the network), we define a new centrality metric that indicates the importance degree of a node in SIoT. Embedding a Learning Automata in each object, a distributed LA approach is proposed for the selection of the most influential nodes in the network. Starting from a requesting object, the proposed DLA_N algorithm learns to select a path containing objects with a high centrality metric. The distributed nature of our navigation results in high scalability and low navigation time. The results of performance evaluation indicate that the proposed method outperforms existing methods in the literature.

The popularity of social networks has grown manifolds in recent years because of various activities like fast propagation of ideas, publicity, and news. Influence maximization (IM) is one of the most highly studied problems in the field of social network analysis due to its business values. Influence maximization aims to identify influential nodes that can spread the information to the maximum number of nodes in the network through diffusion cascade. Traditional methods for IM include centrality based and greedy based measures. However, each method has some limitations. Recently, some methods of IM are introduced, which consider the presence of community structure in networks. Community structure has a significant impact on information diffusion, because of dense connections between the nodes in the community. In this paper, we propose a novel influence maximization algorithm using node seeding, label propagation, and community detection. We first use extended h-index centrality to detect the seed nodes and then use the label propagation technique to detect communities. Further, we merge smaller and related communities to a larger community with the help of a relationship matrix. Finally, top-k influential nodes from these communities are identified. These ideas lead to our proposed algorithm: Influence Maximization using Extended h-index, and Label Propagation with Relationship matrix (IM-ELPR). We adopt Independent Cascade (IC) information diffusion model to spread the information originating from chosen influential nodes. The proposed algorithm intends to identify influential nodes from different communities globally and does not depend on the specific community’s antecedent structural information. Experimental results performed on several real-life data sets reveal that the proposed algorithm performs better than many other existing popular algorithms.

Based on relevant data from 2014 to 2018, we constructed a spatial network of haze pollution in the Pearl River Delta (PRD) region by using a modified gravity model, explored the network structure characteristics and influencing factors of urban agglomeration with the SNA and the QAP method, and discussed the population mobility effect brought by haze pollution. The results showed that the PRD region has spatial correlation in terms of haze pollution. Guangzhou was in the central position of the spatial network, yet Shenzhen and Foshan played a connecting role in the network. Economic development, urbanization, and environmental protection had significant positive impacts on the haze pollution spatial network. The results of the double-threshold regression showed that the city’s status in the haze pollution network represented by centrality had a positive impact on population mobility.

Increasing evidence of women’s under-representation in some scientific disciplines is prompting researchers to expand our understanding of this social phenomenon. Moreover, any countermeasures proposed to eliminate this under-representation should be tailored to the actual reasons for this different participation. Here, we take a multi-dimensional approach to assessing gender differences in science by representing scientific communities as social networks, and using data analytics, complexity science methods, and semantic methods to measure gender differences in the context, the attitude and the success of scientists. We apply this approach to four scientific communities in the two fields of computer science and information systems using the network of authors at four different conferences. For each discipline, one conference is based in Italy and attracts mostly Italians, while one conference is international in both location and participants. The present paper provides evidence against common narratives that women’s under-representation is due to women’s limited skills and/or less social centrality.

In this paper, I tackle the center-periphery dynamics under planetary urbanization by focusing on Gebze, the gigantic industrial city just outside of Istanbul’s administrative borders. Through an e...