An Ant-Based Algorithm with Local Optimization for Community Detection in Large-Scale Networks

In this paper, we propose a multi-layer ant-based algorithm (MABA), which detects communities from networks by means of locally optimizing modularity using individual ants. The basic version of MABA, namely SABA, combines a self-avoiding label propagation technique with a simulated annealing strategy for ant diffusion in networks. Once the communities are found by SABA, this method can be reapplied to a higher level network where each obtained community is regarded as a new vertex. The aforementioned process is repeated iteratively, and this corresponds to MABA. Thanks to the intrinsic multi-level nature of our algorithm, it possesses the potential ability to unfold multi-scale hierarchical structures. Furthermore, MABA has the ability that mitigates the resolution limit of modularity. The proposed MABA has been evaluated on both computer-generated benchmarks and widely used real-world networks, and has been compared with a set of competitive algorithms. Experimental results demonstrate that MABA is both effective and efficient (in near linear time with respect to the size of network) for discovering communities.

[1]  Chang Honghao,et al.  Community detection using Ant Colony Optimization , 2013, 2013 IEEE Congress on Evolutionary Computation.

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

[3]  Qiang Wang,et al.  Email Community Detection Using Artificial Ant Colony Clustering , 2007, APWeb/WAIM Workshops.

[4]  Sune Lehmann,et al.  Link communities reveal multiscale complexity in networks , 2009, Nature.

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

[6]  Jure Leskovec,et al.  Statistical properties of community structure in large social and information networks , 2008, WWW.

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

[8]  A. Sima Etaner-Uyar,et al.  An efficient community detection method using parallel clique-finding ants , 2010, IEEE Congress on Evolutionary Computation.

[9]  P. Ronhovde,et al.  Multiresolution community detection for megascale networks by information-based replica correlations. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[10]  Sercan Sadi,et al.  COMMUNITY DETECTION USING ANT COLONY OPTIMIZATION TECHNIQUES , 2009 .

[11]  L. D. Costa.  Hub-Based Community Finding , 2004, cond-mat/0405022.

[12]  G. Theraulaz,et al.  Inspiration for optimization from social insect behaviour , 2000, Nature.

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

[14]  M. Hasler,et al.  Network community-detection enhancement by proper weighting. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[15]  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.

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

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

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

[19]  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.

[20]  Lada A. Adamic,et al.  Power-Law Distribution of the World Wide Web , 2000, Science.

[21]  Dayou Liu,et al.  Genetic Algorithm with Local Search for Community Mining in Complex Networks , 2010, 2010 22nd IEEE International Conference on Tools with Artificial Intelligence.

[22]  Lian Liu,et al.  Finding Closely Communicating Community Based on Ant Colony Clustering Model , 2010, 2010 International Conference on Artificial Intelligence and Computational Intelligence.

[23]  Dayou Liu,et al.  Ant Colony Optimization with a New Random Walk Model for Community Detection in Complex Networks , 2011, Adv. Complex Syst..

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

[25]  Jiming Liu,et al.  Community Mining from Signed Social Networks , 2007, IEEE Transactions on Knowledge and Data Engineering.

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

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

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

[29]  R. Guimerà,et al.  Functional cartography of complex metabolic networks , 2005, Nature.

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

[31]  U. Brandes,et al.  Maximizing Modularity is hard , 2006, physics/0608255.

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

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

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

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

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