Identification of influential spreaders in online social networks using interaction weighted K-core decomposition method

Online social networks (OSNs) have become a vital part of everyday living. OSNs provide researchers and scientists with unique prospects to comprehend individuals on a scale and to analyze human behavioral patterns. Influential spreaders identification is an important subject in understanding the dynamics of information diffusion in OSNs. Targeting these influential spreaders is significant in planning the techniques for accelerating the propagation of information that is useful for various applications, such as viral marketing applications or blocking the diffusion of annoying information (spreading of viruses, rumors, online negative behaviors, and cyberbullying). Existing K-core decomposition methods consider links equally when calculating the influential spreaders for unweighted networks. Alternatively, the proposed link weights are based only on the degree of nodes. Thus, if a node is linked to high-degree nodes, then this node will receive high weight and is treated as an important node. Conversely, the degree of nodes in OSN context does not always provide accurate influence of users. In the present study, we improve the K-core method for OSNs by proposing a novel link-weighting method based on the interaction among users. The proposed method is based on the observation that the interaction of users is a significant factor in quantifying the spreading capability of user in OSNs. The tracking of diffusion links in the real spreading dynamics of information verifies the effectiveness of our proposed method for identifying influential spreaders in OSNs as compared with degree centrality, PageRank, and original K-core.

[1]  Wei Huang,et al.  Rumor spreading model with consideration of forgetting mechanism: A case of online blogging LiveJournal , 2011 .

[2]  P. Lazarsfeld,et al.  6. Katz, E. Personal Influence: The Part Played by People in the Flow of Mass Communications , 1956 .

[3]  Qi He,et al.  TwitterRank: finding topic-sensitive influential twitterers , 2010, WSDM '10.

[4]  Boleslaw K. Szymanski,et al.  Social ranking techniques for the web , 2013, 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013).

[5]  Vladimir Batagelj,et al.  An O(m) Algorithm for Cores Decomposition of Networks , 2003, ArXiv.

[6]  M. Macy,et al.  Complex Contagions and the Weakness of Long Ties1 , 2007, American Journal of Sociology.

[7]  Yong Deng,et al.  Weighted k-shell decomposition for complex networks based on potential edge weights , 2015 .

[8]  Krishna P. Gummadi,et al.  Measuring User Influence in Twitter: The Million Follower Fallacy , 2010, ICWSM.

[9]  Jacob Goldenberg,et al.  Talk of the Network: A Complex Systems Look at the Underlying Process of Word-of-Mouth , 2001 .

[10]  U. Brandes.  A faster algorithm for betweenness centrality , 2001 .

[11]  Hiroyuki Kitagawa,et al.  TURank: Twitter User Ranking Based on User-Tweet Graph Analysis , 2010, WISE.

[12]  Zhiming Zheng,et al.  Exploring the Complex Pattern of Information Spreading in Online Blog Communities , 2015, PloS one.

[13]  Matthew Richardson,et al.  Mining knowledge-sharing sites for viral marketing , 2002, KDD.

[14]  Myra Spiliopoulou,et al.  A framework for validating the merit of properties that predict the influence of a twitter user , 2015, Expert Syst. Appl..

[15]  Qiang Guo,et al.  Ranking the spreading influence in complex networks , 2013, ArXiv.

[16]  M. De Domenico,et al.  The Anatomy of a Scientific Rumor , 2013, Scientific Reports.

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

[18]  Kyomin Jung,et al.  Prominent Features of Rumor Propagation in Online Social Media , 2013, 2013 IEEE 13th International Conference on Data Mining.

[19]  A. Vespignani,et al.  Competition among memes in a world with limited attention , 2012, Scientific Reports.

[20]  Daniel G. Goldstein,et al.  The structure of online diffusion networks , 2012, EC '12.

[21]  Mohand Boughanem,et al.  Active Microbloggers: Identifying Influencers, Leaders and Discussers in Microblogging Networks , 2012, SPIRE.

[22]  Jon M. Kleinberg,et al.  Center of Attention: How Facebook Users Allocate Attention across Friends , 2011, ICWSM.

[23]  Boleslaw K. Szymanski,et al.  Threshold-limited spreading in social networks with multiple initiators , 2013, Scientific Reports.

[24]  Duncan J Watts,et al.  A simple model of global cascades on random networks , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[25]  Balaji Rajagopalan,et al.  Knowledge-sharing and influence in online social networks via viral marketing , 2003, CACM.

[26]  Gourab Ghoshal,et al.  Ranking stability and super-stable nodes in complex networks. , 2011, Nature communications.

[27]  Jon M. Kleinberg,et al.  Sequential Influence Models in Social Networks , 2010, ICWSM.

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

[29]  Bin Zhou,et al.  Measuring the spreadability of users in microblogs , 2013, Journal of Zhejiang University SCIENCE C.

[30]  P. Lazarsfeld,et al.  Personal Influence: The Part Played by People in the Flow of Mass Communications , 1956 .

[31]  Erica Suyeon Kim,et al.  An Analytical Way to Find Influencers on Social Networks and Validate their Effects in Disseminating Social Games , 2009, 2009 International Conference on Advances in Social Network Analysis and Mining.

[32]  Filippo Menczer,et al.  Virality Prediction and Community Structure in Social Networks , 2013, Scientific Reports.

[33]  Jacob Ratkiewicz,et al.  Detecting and Tracking Political Abuse in Social Media , 2011, ICWSM.

[34]  Duanbing Chen,et al.  Identifying Influential Spreaders by Weighted LeaderRank , 2013, ArXiv.

[35]  Yamir Moreno,et al.  Absence of influential spreaders in rumor dynamics , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[36]  Yicheng Zhang,et al.  Identifying influential nodes in complex networks , 2012 .

[37]  Hernán A. Makse,et al.  Influence maximization in complex networks through optimal percolation , 2015, Nature.

[38]  Shaoyin Cheng,et al.  InfluenceRank: An Efficient Social Influence Measurement for Millions of Users in Microblog , 2012, 2012 Second International Conference on Cloud and Green Computing.

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

[40]  J. Kleinberg.  Algorithmic Game Theory: Cascading Behavior in Networks: Algorithmic and Economic Issues , 2007 .

[41]  Hernán A. Makse,et al.  Spreading dynamics in complex networks , 2013, ArXiv.

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

[43]  Frank Schweitzer,et al.  A k-shell decomposition method for weighted networks , 2012, ArXiv.

[44]  Pasquale De Meo,et al.  Extraction and Analysis of Facebook Friendship Relations , 2012, Computational Social Networks.