Community detection in complex networks using edge-deleting with restrictions

Abstract Community detection is an important task with great practical significance for understanding the structure and function of complex networks in various fields. As the real world networks become larger and more complex, it is a challenge to achieve high quality of community partitioning. In order to identify community structure more effectively in complex networks, a new algorithm, which iteratively deletes edges with restrictions is proposed in this paper. The algorithm first makes use of the connection strength between vertices to divide the original network into some strongly connected communities with optimal modularity by the improved edge-deleting process, and finally reconnects the isolated vertices to initial communities for optimizing community structure. Experiments on the real-world and synthetic networks prove that the proposed algorithm achieves a competitive performance compared with other reference algorithms.

[1]  Xingyuan Wang,et al.  Community detection using local neighborhood in complex networks , 2015 .

[2]  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.

[3]  François Fouss,et al.  Modularity-Driven Kernel k-means for Community Detection , 2017, ICANN.

[4]  Chengcui Zhang,et al.  A fast parallel modularity optimization algorithm (FPMQA) for community detection in online social network , 2013, Knowl. Based Syst..

[5]  Marko Bajec,et al.  Unfolding communities in large complex networks: Combining defensive and offensive label propagation for core extraction , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[6]  Steven P Gross,et al.  Modularity optimization by conformational space annealing. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

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

[8]  Éric Gaussier,et al.  A Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for Evaluation , 2005, ECIR.

[9]  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.

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

[11]  Suely Oliveira,et al.  A Multilevel Approach to Identify Functional Modules in a Yeast Protein-Protein Interaction Network , 2006, International Conference on Computational Science.

[12]  L. R. Dice.  Measures of the Amount of Ecologic Association Between Species , 1945 .

[13]  Pablo M. Gleiser,et al.  Community Structure in Jazz , 2003, Adv. Complex Syst..

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

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

[16]  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.

[17]  Tao Wang,et al.  A community detection method based on local similarity and degree clustering information , 2018 .

[18]  Jianzhi Zhang,et al.  In Search of the Biological Significance of Modular Structures in Protein Networks , 2007, PLoS Comput. Biol..

[19]  Vladlen Koltun,et al.  Robust continuous clustering , 2017, Proceedings of the National Academy of Sciences.

[20]  Hongyu Zhao,et al.  Normalized modularity optimization method for community identification with degree adjustment. , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

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

[22]  Dorothea Wagner,et al.  Approximating Clustering Coefficient and Transitivity , 2005, J. Graph Algorithms Appl..

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

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

[25]  Yao Wang,et al.  LED: A fast overlapping communities detection algorithm based on structural clustering , 2016, Neurocomputing.

[26]  Natarajan Meghanathan,et al.  A Greedy Algorithm for Neighborhood Overlap-Based Community Detection , 2016, Algorithms.

[27]  Yannis Manolopoulos,et al.  Prefetching in Content Distribution Networks via Web Communities Identification and Outsourcing , 2007, World Wide Web.

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

[29]  Fu Li-dong.  Kernel k-means Clustering Algorithm for Detecting Communities in Complex Networks , 2010 .

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

[31]  Masaru Kitsuregawa,et al.  A Graph Based Approach to Extract a Neighborhood Customer Community for Collaborative Filtering , 2002, DNIS.

[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.