Community cores expansion for overlapping community detection in complex networks

The identification of communities has attracted considerable attentions in the last few years. We propose a novel heuristic algorithm for overlapping community detection based on community cores in complex networks. We introduce a novel clique percolation algorithm and maximize cliques in the finding overlapping communities (node covers) in graphs. We show how vertices can be used to quantify types of local structure presented in a community and identify group nodes that have similar roles in relation to their neighbors. We compare the approach with other three common algorithms in the analysis of the Zachary’s karate club network and the dolphins network. Experimental results in real-world and synthetic datasets (Lancichinetti–Fortunato–Radicchi (LFR) benchmark networks [A. Lancichinetti and S. Fortunato, Phys. Rev. E 80 (2009) 016118]) demonstrate the model has scalability and is well behaved.

[1]  Erik M Bollt,et al.  Local method for detecting communities. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

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

[3]  Stefan Bornholdt,et al.  Detecting fuzzy community structures in complex networks with a Potts model. , 2004, Physical review letters.

[4]  M. Newman,et al.  Hierarchical structure and the prediction of missing links in networks , 2008, Nature.

[5]  Luonan Chen,et al.  Quantitative function for community detection. , 2008 .

[6]  V. Carchiolo,et al.  Extending the definition of modularity to directed graphs with overlapping communities , 2008, 0801.1647.

[7]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[8]  Leon Danon,et al.  Comparing community structure identification , 2005, cond-mat/0505245.

[9]  D. Lusseau,et al.  The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations , 2003, Behavioral Ecology and Sociobiology.

[10]  M. Newman.  Analysis of weighted networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[11]  Mao-Bin Hu,et al.  Detect overlapping and hierarchical community structure in networks , 2008, ArXiv.

[12]  C. Bron,et al.  Algorithm 457: finding all cliques of an undirected graph , 1973 .

[13]  G. Bianconi.  The entropy of randomized network ensembles , 2007, 0708.0153.

[14]  T. Vicsek,et al.  Clique percolation in random networks. , 2005, Physical review letters.

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

[16]  Boleslaw K. Szymanski,et al.  Overlapping community detection in networks: The state-of-the-art and comparative study , 2011, CSUR.

[17]  A. Clauset.  Finding local community structure in networks. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[18]  Martin Rosvall,et al.  Maps of random walks on complex networks reveal community structure , 2007, Proceedings of the National Academy of Sciences.

[19]  Seungsik Min,et al.  Macroscopic analyses of communicability structures in complex networks , 2016 .

[20]  Benjamin H. Good,et al.  Performance of modularity maximization in practical contexts. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[21]  Illés J. Farkas,et al.  CFinder: locating cliques and overlapping modules in biological networks , 2006, Bioinform..

[22]  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.

[23]  W. Zachary,et al.  An Information Flow Model for Conflict and Fission in Small Groups , 1977, Journal of Anthropological Research.

[24]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[25]  J. A. Almendral,et al.  Explosive transitions in complex networks’ structure and dynamics: Percolation and synchronization , 2016, 1610.01361.

[26]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[27]  Xuelong Li,et al.  Overlapping Community Detection for Multimedia Social Networks , 2017, IEEE Transactions on Multimedia.

[28]  Yong Zhou,et al.  A Node Influence Based Label Propagation Algorithm for Community Detection in Networks , 2014, TheScientificWorldJournal.

[29]  Haewoon Kwak,et al.  Consistent Community Identification in Complex Networks , 2009, 0910.1508.

[30]  Andrea Lancichinetti,et al.  Detecting the overlapping and hierarchical community structure in complex networks , 2008, 0802.1218.

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

[32]  A. Arenas,et al.  Community detection in complex networks using extremal optimization. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[33]  Claudio Castellano,et al.  Defining and identifying communities in networks. , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[34]  Konstantin Avrachenkov,et al.  Cooperative Game Theory Approaches for Network Partitioning , 2017, COCOON.

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

[36]  I. Ispolatov,et al.  Finding mesoscopic communities in sparse networks , 2005, Journal of statistical mechanics.

[37]  Xiang-Sun Zhang,et al.  Detecting community structure in complex networks based on a measure of information discrepancy , 2008 .

[38]  Andrea Lancichinetti,et al.  Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.