A new hierarchical method to find community structure in networks

Abstract Community structure is very important to understand a network which represents a context. Many community detection methods have been proposed like hierarchical methods. In our study, we propose a new hierarchical method for community detection in networks based on genetic algorithm. In this method we use genetic algorithm to split a network into two networks which maximize the modularity. Each new network represents a cluster (community). Then we repeat the splitting process until we get one node at each cluster. We use the modularity function to measure the strength of the community structure found by our method, which gives us an objective metric for choosing the number of communities into which a network should be divided. We demonstrate that our method are highly effective at discovering community structure in both computer-generated and real-world network data.

[1]  Réka Albert,et al.  Near linear time algorithm to detect community structures in large-scale networks. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[2]  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.

[3]  B. Grenfell,et al.  Mean-field-type equations for spread of epidemics: the ‘small world’ model , 1999 .

[4]  Joshua D. Knowles,et al.  An Evolutionary Approach to Multiobjective Clustering , 2007, IEEE Transactions on Evolutionary Computation.

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

[6]  Ronghua Shang,et al.  Community detection based on modularity and an improved genetic algorithm , 2013 .

[7]  S. Redner.  How popular is your paper? An empirical study of the citation distribution , 1998, cond-mat/9804163.

[8]  Yang Wang,et al.  Overlapping community detection through an improved multi-objective quantum-behaved particle swarm optimization , 2015, Journal of Heuristics.

[9]  M. Newman,et al.  Epidemics and percolation in small-world networks. , 1999, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[10]  Matthieu Latapy,et al.  Computing Communities in Large Networks Using Random Walks , 2004, J. Graph Algorithms Appl..

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

[12]  Yangyang Li,et al.  Large-scale community detection based on node membership grade and sub-communities integration , 2015, Physica A: Statistical Mechanics and its Applications.

[13]  J. Reichardt,et al.  Statistical mechanics of community detection. , 2006, 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]  Andrei Z. Broder,et al.  Graph structure in the Web , 2000, Comput. Networks.

[16]  D. Fell,et al.  The small world inside large metabolic networks , 2000, Proceedings of the Royal Society of London. Series B: Biological Sciences.

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

[18]  Yu Xue,et al.  A community integration strategy based on an improved modularity density increment for large-scale networks , 2017 .

[19]  Ronghua Shang,et al.  A multiobjective evolutionary algorithm to find community structures based on affinity propagation , 2016 .

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

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

[22]  Albert-László Barabási,et al.  Internet: Diameter of the World-Wide Web , 1999, Nature.

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

[24]  Michalis Faloutsos,et al.  On power-law relationships of the Internet topology , 1999, SIGCOMM '99.

[25]  M. Newman,et al.  The structure of scientific collaboration networks. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[26]  F. Radicchi,et al.  Benchmark graphs for testing community detection algorithms. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[27]  R. Albert,et al.  The large-scale organization of metabolic networks , 2000, Nature.

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