Kunhe Yang
ABSTRACT
Research on community structures promotes the discovery of the relationship between network structure and functionality, while community detection is the foundation and core of community structure research. In this study, a community division algorithm is proposed based on a hierarchical division; a modified Jaccard similarity coeffcient is employed to detect the edges between the nodes; the network is decomposed by deleting edges between nodes to detect community structures within a network. According to the experiments on the datasets of artificial networks and real networks, this algorithm can yield accurate and meaningful community structure without prior information, of which the accuracy exceeds or approaches to that of classic community detection algorithms. In addition, compared with the classic GN splitting algorithm, the proposed algorithm produces a division of community structures that is consistent with that of GN algorithm, with a significantly improved time performance.
- S. H. Strogatz. 2001. Exploring complex networks. Nature (2001).Google Scholar
- M. E. J. Newman and M. Girvan. 2004. Finding and evaluating community structure in networks. Physical Review E Statistical Nonlinear and Soft Matter Physics 69, 2 (2004), 026113.Google ScholarCross Ref
- Min Liang, Liangshan Shao, Yonggang Zhao, School Of Software, and Liaoning Technical University. 2015. Community detection based on node similarity. Computer Engineering and Applications (2015).Google Scholar
- Clauset Aaron, Newman, and Moore Cristopher. 2004. Finding community structure in very large networks. Physical Review E 70, 2 (2004), 066111.Google ScholarCross Ref
- Maria C. V. Nascimento. 2014. Community detection in networks via a spectral heuristic based on the clustering coefficient. Discrete Applied Mathematics 176, 3 (2014), 89--99. Google ScholarDigital Library
- Gergely Palla, Imre, Illés Farkas, and Tamás Vicsek. 2005. Uncovering the overlapping community structure of complex networks in nature and society. nature 435, 7043 (2005), 814.Google Scholar
- Brian Karrer and Mark E. J. Newman. 2011. Stochastic blockmodels and community structure in networks. Physical review. E, Statistical, nonlinear, and soft matter physics 83-1 Pt 2 (2011), 016107.Google Scholar
- Usha Nandini Raghavan, Réka Albert, and Soundar Kumara. 2007. Near linear time algorithm to detect community structures in large-scale networks. Physical review E 76, 3 (2007), 036106.Google Scholar
- Gang Sun Peng and Yang Yang. 2013. Methods to find community based on edge centrality. Physica A Statistical Mechanics and Its Applications 392, 9 (2013), 1977--1988.Google ScholarCross Ref
- Maurice Roux. 2015. A comparative study of divisive hierarchical clustering algorithms. (2015). Google ScholarDigital Library
- M. Girvan and M. E. J. Newman. 2002. Community structure in social and biological networks. 99, 12 (2002), 7821--7826.Google Scholar
- Joshua R. Tyler, Dennis M. Wilkinson, and Bernardo A. Huberman. 2003. Email as Spectroscopy: Automated Discovery of Community Structure within Organizations.Google ScholarDigital Library
- Dennis M Wilkinson and Bernardo A Huberman. 2004. A method for finding communities of related genes. proceedings of the national Academy of sciences 101, suppl 1 (2004), 5241--5248.Google ScholarCross Ref
- Oded Green and David A. Bader. 2013. Faster Betweenness Centrality Based on Data Structure Experimentation. In Procedia Computer Science. 399--408.Google Scholar
- L. Li, S. Lu, S. Li, Z. Xia, and Y. Yi. 2014. A community divisive algorithm using local weak links. In International Conference on It Convergence and Security.Google Scholar
- Paul Jaccard. 1912. The Distribution of Flora in the Alpine Zone. New Phytologist 11 (02-1912), 37--50.Google Scholar
- Chuantao Yin, Shuaibing Zhu, Chen Hui, Bingxue Zhang, and Bertrand David. 2015. A Method for Community Detection of Complex Networks Based on Hierarchical Clustering. International Journal of Distributed Sensor Networks 2015 (2015), 137.Google ScholarDigital Library
- Leon Danon, Albert Dazguilera, Jordi Duch, and Alex Arenas. 2005. Comparing community structure identification. Journal of Statistical Mechanics 2005, 09 (2005), 09008.Google ScholarCross Ref
- Lancichinetti Andrea, Fortunato Santo, and Radicchi Filippo.2008. Benchmark graphs for testing community detection algorithms. Physical Review E Statistical Nonlinear and Soft Matter Physics 78, 4 Pt 2 (2008), 046110.Google ScholarCross Ref
- Wayne W. Zachary. 1977. An Information Flow Model for Conflict and Fission in Small Groups. Journal of Anthropological Research 33, 4 (1977), 452--473.Google ScholarCross Ref
- David Lusseau, Karsten Schneider, Oliver J. Boisseau, Patti Haase, Elisabeth Slooten, and Steve M. Dawson. 2003. The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations. Behavioral Ecology and Sociobiology 54, 4 (2003), 396--405.Google ScholarCross Ref
- Mark EJ Newman. 2006. Modularity and community structure in networks. Proceedings of the national academy of sciences 103, 23 (2006), 8577--8582.Google ScholarCross Ref
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