Community cores in evolving networks

Community structure is a key property of complex networks. Many algorithms have been proposed to automatically detect communities in static networks but few studies have considered the detection and tracking of communities in an evolving network. Tracking the evolution of a given community over time requires a clustering algorithm that produces stable clusters. However, most community detection algorithms are very unstable and therefore unusable for evolving networks. In this paper, we apply the methodology proposed in [seifi2012] to detect what we call community cores in evolving networks. We show that cores are much more stable than "classical" communities and that we can overcome the disadvantages of the stabilized methods.

[1]  M. Newman,et al.  Finding community structure in networks using the eigenvectors of matrices. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[2]  Jean-Loup Guillaume,et al.  Stable Community Cores in Complex Networks , 2012, CompleNet.

[3]  Olivier Bonaventure,et al.  Extracting Intra-domain Topology from mrinfo Probing , 2010, PAM.

[4]  Athena Vakali,et al.  Capturing Social Data Evolution Using Graph Clustering , 2013, IEEE Internet Computing.

[5]  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.

[6]  A Díaz-Guilera,et al.  Self-similar community structure in a network of human interactions. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[7]  S. Fortunato,et al.  Resolution limit in community detection , 2006, Proceedings of the National Academy of Sciences.

[8]  Lucas Antiqueira,et al.  Analyzing and modeling real-world phenomena with complex networks: a survey of applications , 2007, 0711.3199.

[9]  Yun Chi,et al.  Analyzing communities and their evolutions in dynamic social networks , 2009, TKDD.

[10]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[11]  Jean-Loup Guillaume,et al.  Static community detection algorithms for evolving networks , 2010, 8th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks.

[12]  Myra Spiliopoulou,et al.  MONIC: modeling and monitoring cluster transitions , 2006, KDD '06.

[13]  Andrea Lancichinetti,et al.  Community detection algorithms: a comparative analysis: invited presentation, extended abstract , 2009, VALUETOOLS.

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

[15]  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.