Identifying Influencers in Social Networks

Social network analysis is a multidisciplinary research covering informatics, mathematics, sociology, management, psychology, etc. In the last decade, the development of online social media has provided individuals with a fascinating platform of sharing knowledge and interests. The emergence of various social networks has greatly enriched our daily life, and simultaneously, it brings a challenging task to identify influencers among multiple social networks. The key problem lies in the various interactions among individuals and huge data scale. Aiming at solving the problem, this paper employs a general multilayer network model to represent the multiple social networks, and then proposes the node influence indicator merely based on the local neighboring information. Extensive experiments on 21 real-world datasets are conducted to verify the performance of the proposed method, which shows superiority to the competitors. It is of remarkable significance in revealing the evolutions in social networks and we hope this work will shed light for more and more forthcoming researchers to further explore the uncharted part of this promising field.

[1]  Bo Hu,et al.  Efficient routing on complex networks. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[2]  Mason A. Porter,et al.  Multilayer networks , 2013, J. Complex Networks.

[3]  P. Pattison,et al.  New Specifications for Exponential Random Graph Models , 2006 .

[4]  Wei Yu,et al.  Diversity-maintained differential evolution embedded with gradient-based local search , 2013, Soft Comput..

[5]  Albert-László Barabási,et al.  Controllability of complex networks , 2011, Nature.

[6]  J. E. Hirsch,et al.  An index to quantify an individual's scientific research output , 2005, Proc. Natl. Acad. Sci. USA.

[7]  Ignacio Marín,et al.  Surprise maximization reveals the community structure of complex networks , 2013, Scientific Reports.

[8]  P. Bonacich.  Factoring and weighting approaches to status scores and clique identification , 1972 .

[9]  Leonard M. Freeman,et al.  A set of measures of centrality based upon betweenness , 1977 .

[10]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[11]  Ryan A. Rossi,et al.  The Network Data Repository with Interactive Graph Analytics and Visualization , 2015, AAAI.

[12]  A. Arenas,et al.  Community detection in complex networks using extremal optimization. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[13]  Leandros Tassiulas,et al.  Identifying Influential Spreaders in Complex Multilayer Networks: A Centrality Perspective , 2019, IEEE Transactions on Network Science and Engineering.

[14]  L. Freeman.  Centrality in social networks conceptual clarification , 1978 .

[15]  Donald E. Knuth.  The Stanford GraphBase: a platform for combinatorial algorithms , 1993, SODA '93.

[16]  S. Shen-Orr,et al.  Network motifs in the transcriptional regulation network of Escherichia coli , 2002, Nature Genetics.

[17]  Michalis Vazirgiannis,et al.  Locating influential nodes in complex networks , 2016, Scientific Reports.

[18]  Mitchell D. Silber.  The Al Qaeda Factor: Plots Against the West , 2011 .

[19]  Alessandro Vespignani,et al.  Reaction–diffusion processes and metapopulation models in heterogeneous networks , 2007, cond-mat/0703129.

[20]  Konstantin Avrachenkov,et al.  Cooperative Game Theory Approaches for Network Partitioning , 2017, COCOON.

[21]  Ali Harounabadi,et al.  Identifying Top-k Most Influential Nodes by using the Topological Diffusion Models in the Complex Networks , 2017 .

[22]  M. Kendall.  A NEW MEASURE OF RANK CORRELATION , 1938 .

[23]  Xiaoqi Ma,et al.  Identifying influential spreaders by gravity model , 2019, Scientific Reports.

[24]  Z. Wang,et al.  The structure and dynamics of multilayer networks , 2014, Physics Reports.

[25]  Albert Solé-Ribalta,et al.  Navigability of interconnected networks under random failures , 2013, Proceedings of the National Academy of Sciences.

[26]  M E J Newman,et al.  Modularity and community structure in networks. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[27]  John F. Padgett,et al.  Robust Action and the Rise of the Medici, 1400-1434 , 1993, American Journal of Sociology.

[28]  Zhixiao Wang,et al.  Superspreaders and superblockers based community evolution tracking in dynamic social networks , 2020, Knowl. Based Syst..

[29]  Zhiming Zheng,et al.  Searching for superspreaders of information in real-world social media , 2014, Scientific Reports.

[30]  A. Vespignani,et al.  The architecture of complex weighted networks. , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[31]  Jure Leskovec,et al.  node2vec: Scalable Feature Learning for Networks , 2016, KDD.

[32]  Hui Gao,et al.  Identifying Influential Nodes in Large-Scale Directed Networks: The Role of Clustering , 2013, PloS one.

[33]  W. Zachary,et al.  An Information Flow Model for Conflict and Fission in Small Groups , 1977, Journal of Anthropological Research.

[34]  Masahiro Kimura,et al.  Resampling-based predictive simulation framework of stochastic diffusion model for identifying top-K influential nodes , 2013, International Journal of Data Science and Analytics.

[35]  P. Bonacich.  Power and Centrality: A Family of Measures , 1987, American Journal of Sociology.

[36]  Francisco Aparecido Rodrigues,et al.  Network Centrality: An Introduction , 2018, A Mathematical Modeling Approach from Nonlinear Dynamics to Complex Systems.

[37]  Alireza Abdollahpouri,et al.  BridgeRank: A novel fast centrality measure based on local structure of the network , 2017 .

[38]  Chuang Ma,et al.  Identifying influential spreaders in complex networks based on gravity formula , 2015, ArXiv.

[39]  Hyojin Kim,et al.  WormNet v3: a network-assisted hypothesis-generating server for Caenorhabditis elegans , 2014, Nucleic Acids Res..

[40]  Massimiliano Zanin,et al.  Emergence of network features from multiplexity , 2012, Scientific Reports.

[41]  David Krackhardt,et al.  Cognitive social structures , 1987 .

[42]  Barbora Micenková,et al.  Combinatorial Analysis of Multiple Networks , 2013, ArXiv.

[43]  Sergey Brin,et al.  The Anatomy of a Large-Scale Hypertextual Web Search Engine , 1998, Comput. Networks.

[44]  Lev Muchnik,et al.  Identifying influential spreaders in complex networks , 2010, 1001.5285.

[45]  Mike Tyers,et al.  BioGRID: a general repository for interaction datasets , 2005, Nucleic Acids Res..

[46]  D. Krackhardt.  Assessing the political landscape: Structure, cognition, and power in organizations. , 1990 .

[47]  Yi-Cheng Zhang,et al.  Leaders in Social Networks, the Delicious Case , 2011, PloS one.

[48]  B. Kapferer.  Strategy and transaction in an African factory: African workers and Indian management in a Zambian town , 1972 .

[49]  A. Barabasi,et al.  Lethality and centrality in protein networks , 2001, Nature.

[50]  Xiufen Zou,et al.  Identifying key nodes in multilayer networks based on tensor decomposition. , 2017, Chaos.

[51]  Osman Yagan,et al.  Information Propagation in Clustered Multilayer Networks , 2015, IEEE Transactions on Network Science and Engineering.

[52]  M. McPherson,et al.  Birds of a Feather: Homophily in Social Networks , 2001 .

[53]  Pablo M. Gleiser,et al.  Community Structure in Jazz , 2003, Adv. Complex Syst..

[54]  D. Lusseau,et al.  The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations , 2003, Behavioral Ecology and Sociobiology.

[55]  Alexandre Arenas,et al.  Centralities of Nodes and Influences of Layers in Large Multiplex Networks , 2017, J. Complex Networks.

[56]  E. Lazega.  Introduction : Collegial Phenomenon : The Social Mechanisms of Cooperation Among Peers in a Corporate Law Partnership , 2001 .

[57]  Stephen P. Borgatti,et al.  Centrality and network flow , 2005, Soc. Networks.

[58]  Weijia Jia,et al.  Influence analysis in social networks: A survey , 2018, J. Netw. Comput. Appl..

[59]  Réka Albert,et al.  Structural vulnerability of the North American power grid. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[60]  Herbert W. Hethcote,et al.  The Mathematics of Infectious Diseases , 2000, SIAM Rev..

[61]  Fan Yang,et al.  Identification of top-k influential nodes based on enhanced discrete particle swarm optimization for influence maximization , 2019, Physica A: Statistical Mechanics and its Applications.