A New Randomized Algorithm for Community Detection in Large Networks

Abstract The problem of community detection (or clustering) in graphs plays an important role in analysis of complex large-scale networks and big data structures, arising in natural, behavioral and engineering sciences. Examples of such networks include, but are not limited to, World Wide Web (WWW) and Internet, social networks, ecological networks and food webs, cellular and molecular ensembles. A community (or a module) in a graph is a subset of its nodes, whose members are "densely" connected to each other yet have relatively few connections with nodes outside this subset. A number of algorithms to subdivide the nodes of large-scale graphs into communities have recently been proposed; many of them hunt for the graph’s partitions of maximal modularity. One of the most efficient graph clustering algorithms of this type is the Multi-Level Aggregation (or "Louvain") method. In this paper, a randomized counterpart of this algorithm is proposed, which provides a comparable "quality" of graph’s clustering, being however much faster on huge graphs. We demonstrate the efficiency of our algorithm, comparing its performance on several "benchmark" large-scale graphs with existing methods.

[1]  Jon Kleinberg,et al.  The Structure of the Web , 2001, Science.

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

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

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

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

[6]  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.

[7]  Chris Hankin,et al.  Fast Multi-Scale Detection of Relevant Communities in Large-Scale Networks , 2013, Comput. J..

[8]  A. Barabasi,et al.  Hierarchical Organization of Modularity in Metabolic Networks , 2002, Science.

[9]  Yasuhiro Fujiwara,et al.  Fast Algorithm for Modularity-Based Graph Clustering , 2013, AAAI.

[10]  C. Lee Giles,et al.  Self-Organization and Identification of Web Communities , 2002, Computer.

[11]  M E J Newman,et al.  Fast algorithm for detecting community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

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

[13]  Amedeo Caflisch,et al.  Multistep greedy algorithm identifies community structure in real-world and computer-generated networks , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

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

[15]  Andreas Geyer-Schulz,et al.  Randomized Greedy Modularity Optimization for Group Detection in Huge Social Networks , 2010 .

[16]  M. Corbetta,et al.  The Dynamical Balance of the Brain at Rest , 2011, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[17]  Viktor K. Prasanna,et al.  Fast parallel algorithm for unfolding of communities in large graphs , 2014, 2014 IEEE High Performance Extreme Computing Conference (HPEC).

[18]  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.

[19]  Mason A. Porter,et al.  Communities in Networks , 2009, ArXiv.

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

[21]  Michalis Vazirgiannis,et al.  Clustering and Community Detection in Directed Networks: A Survey , 2013, ArXiv.

[22]  Stuart A. Rice,et al.  The Identification of Blocs in Small Political Bodies , 1927, American Political Science Review.

[23]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[24]  Yonggang Wen,et al.  Algorithms and Applications for Community Detection in Weighted Networks , 2015, IEEE Transactions on Parallel and Distributed Systems.

[25]  Ken Wakita,et al.  Finding community structure in mega-scale social networks: [extended abstract] , 2007, WWW '07.

[26]  Christos Faloutsos,et al.  SlashBurn: Graph Compression and Mining beyond Caveman Communities , 2014, IEEE Transactions on Knowledge and Data Engineering.

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