Tracking and Predicting Evolution of Social Communities

We develop an algorithmic framework for studying the evolution of communities in social networks. We begin with the theoretical foundation, from which we conclude that the evolution is at most as strong as its weakest link. This allows us to deign an efficient algorithm which identifies all evolutionary sequences in a dynamic social network. We use this algorithm to empirically study community evolution in several large social networks, and in particular, to identify those features of the early stages of a community that indicate whether a community is going to be short-lived or not. Our results show that it is possible to correlate the lifespan of a community with structural parameters of its early evolution, these conclusions are robust across all the social networks that we have investigated.

[1]  Jukka-Pekka Onnela,et al.  Community Structure in Time-Dependent, Multiscale, and Multiplex Networks , 2009, Science.

[2]  Ambuj K. Singh,et al.  RRW: repeated random walks on genome-scale protein networks for local cluster discovery , 2009, BMC Bioinformatics.

[3]  Bo Zhao,et al.  Community evolution detection in dynamic heterogeneous information networks , 2010, MLG '10.

[4]  A. Barabasi,et al.  Quantifying social group evolution , 2007, Nature.

[5]  Bart Selman,et al.  Tracking evolving communities in large linked networks , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[6]  Yun Chi,et al.  Facetnet: a framework for analyzing communities and their evolutions in dynamic networks , 2008, WWW.

[7]  Deepayan Chakrabarti,et al.  Evolutionary clustering , 2006, KDD '06.

[8]  Kathleen M. Carley,et al.  Clearing the FOG: Fuzzy, overlapping groups for social networks , 2008, Soc. Networks.

[9]  Tanya Y. Berger-Wolf,et al.  A framework for community identification in dynamic social networks , 2007, KDD '07.

[10]  T. Vicsek,et al.  Uncovering the overlapping community structure of complex networks in nature and society , 2005, Nature.

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

[12]  Malik Magdon-Ismail,et al.  SSDE-Cluster: Fast Overlapping Clustering of Networks Using Sampled Spectral Distance Embedding and GMMs , 2011, 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing.

[13]  Malik Magdon-Ismail,et al.  Reverse Engineering a Social Agent-Based Hidden Markov Model - VISAGE , 2008, Int. J. Neural Syst..

[14]  Malik Magdon-Ismail,et al.  Efficient Identification of Overlapping Communities , 2005, ISI.

[15]  Srinivasan Parthasarathy,et al.  An event-based framework for characterizing the evolutionary behavior of interaction graphs , 2007, KDD '07.

[16]  Malik Magdon-Ismail,et al.  Reverse Engineering an Agent-Based Hidden Markov Model for Complex Social Systems , 2007, IDEAL.

[17]  Steve Gregory,et al.  Finding Overlapping Communities Using Disjoint Community Detection Algorithms , 2009, CompleNet.

[18]  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.

[19]  Malik Magdon-Ismail,et al.  Overlapping communities in social networks , 2011, Int. J. Soc. Comput. Cyber Phys. Syst..

[20]  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.