Modeling Community Influence in Social Networks with Markov Chains

Social network analysis has been one of the research focuses with the developing of social media services. Social influence also attracts ever-increasing attention and interests from both the sociology and the data mining scholars, but the research of influence is mainly limited in the user level, the community level influence is rarely involved. In this paper, we develop a novel model to analyze and evaluate the community level influence and help the decision maker to comprehend the disparity in influence of different communities. This paper also proposes a simple heuristic node-selection strategy considering the community influence to spread influence, and the experiments of the spread of influence through the social network datasets prove the rationality of the proposed community influence analysis model.

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

[2]  Jure Leskovec,et al.  Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters , 2008, Internet Math..

[3]  Jimeng Sun,et al.  Social influence analysis in large-scale networks , 2009, KDD.

[4]  Wei Chen,et al.  Efficient influence maximization in social networks , 2009, KDD.

[5]  Jacob Goldenberg,et al.  Using Complex Systems Analysis to Advance Marketing Theory Development , 2001 .

[6]  Martin G. Everett,et al.  A Graph-theoretic perspective on centrality , 2006, Soc. Networks.

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

[8]  M. Sarvary,et al.  Network Effects and Personal Influences: The Diffusion of an Online Social Network , 2011 .

[9]  Yiming Yang,et al.  Introducing the Enron Corpus , 2004, CEAS.

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

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

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

[13]  Ian T. Foster,et al.  Mapping the Gnutella Network: Properties of Large-Scale Peer-to-Peer Systems and Implications for System Design , 2002, ArXiv.

[14]  Réka Albert,et al.  Near linear time algorithm to detect community structures in large-scale networks. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[15]  Mark D. Pankin.  MARKOV CHAIN MODELS: THEORETICAL BACKGROUND , 1999 .

[16]  Conditional Markov chain and its application in economic time series analysis , 2011 .

[17]  S. Chib.  Markov Chain Monte Carlo Technology , 2012 .

[18]  Santo Fortunato,et al.  Community detection in graphs , 2009, ArXiv.

[19]  Bin Wu,et al.  A multi-objective approach for community detection in complex network , 2010, IEEE Congress on Evolutionary Computation.

[20]  Tore Opsahl,et al.  Prominence and control: the weighted rich-club effect. , 2008, Physical review letters.

[21]  M. K. Luhandjula,et al.  Multiobjective Stochastic Linear Programming: An Overview , 2011 .

[22]  Jimeng Sun,et al.  A Survey of Models and Algorithms for Social Influence Analysis , 2011, Social Network Data Analytics.

[23]  Conor Hayes,et al.  Cross-Community Influence in Discussion Fora , 2012, ICWSM.

[24]  Ivo J. B. F. Adan,et al.  Exact asymptotics for the stationary distribution of a Markov chain: a production model , 2009, Queueing Syst. Theory Appl..

[25]  Qingfu Zhang,et al.  Community detection in networks by using multiobjective evolutionary algorithm with decomposition , 2012 .

[26]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[27]  Matthew Richardson,et al.  Mining the network value of customers , 2001, KDD '01.