Understanding Community Effects on Information Diffusion

In social network research, community study is one flourishing aspect which leads to insightful solutions to many practical challenges. Despite the ubiquitous existence of communities in social networks and their properties of depicting users and links, they have not been explicitly considered in information diffusion models. Previous studies on social networks discovered that links between communities function differently from those within communities. However, no information diffusion model has yet considered how the community structure affects the diffusion process.

[1]  M. de Rijke,et al.  Formal models for expert finding in enterprise corpora , 2006, SIGIR.

[2]  Laks V. S. Lakshmanan,et al.  CELF++: optimizing the greedy algorithm for influence maximization in social networks , 2011, WWW.

[3]  Carl T. Bergstrom,et al.  The map equation , 2009, 0906.1405.

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

[5]  M. Newman,et al.  Finding community structure in very large networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[6]  Dimitrios Gunopulos,et al.  Finding effectors in social networks , 2010, KDD.

[7]  Andreas Krause,et al.  Cost-effective outbreak detection in networks , 2007, KDD '07.

[8]  Masahiro Kimura,et al.  Learning Continuous-Time Information Diffusion Model for Social Behavioral Data Analysis , 2009, ACML.

[9]  Lada A. Adamic,et al.  The role of social networks in information diffusion , 2012, WWW.

[10]  Yu Wang,et al.  Community-based greedy algorithm for mining top-K influential nodes in mobile social networks , 2010, KDD.

[11]  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.

[12]  Nick Koudas,et al.  Information cascade at group scale , 2013, KDD.

[13]  Wei Chen,et al.  Scalable influence maximization for prevalent viral marketing in large-scale social networks , 2010, KDD.

[14]  Huan Liu,et al.  Exploring Social-Historical Ties on Location-Based Social Networks , 2012, ICWSM.

[15]  Masaru Kitsuregawa,et al.  A Graph Based Approach to Extract a Neighborhood Customer Community for Collaborative Filtering , 2002, DNIS.

[16]  Ling Liu,et al.  Social influence based clustering of heterogeneous information networks , 2013, KDD.

[17]  N. Eagle,et al.  Network Diversity and Economic Development , 2010, Science.

[18]  Yizhou Sun,et al.  SHRINK: a structural clustering algorithm for detecting hierarchical communities in networks , 2010, CIKM.

[19]  Laks V. S. Lakshmanan,et al.  Learning influence probabilities in social networks , 2010, WSDM '10.

[20]  Mark S. Granovetter The Strength of Weak Ties , 1973, American Journal of Sociology.

[21]  Éva Tardos,et al.  Maximizing the Spread of Influence through a Social Network , 2015, Theory Comput..

[22]  M Girvan,et al.  Structure of growing social networks. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[23]  Gábor Csárdi,et al.  The igraph software package for complex network research , 2006 .