Overlapping Community Detection Based on Edge Density Clustering
GUO Kun1,2,3, CHEN Erbao1,2, GUO Wenzhong1,2,3
1.College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116
2.Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing, Fuzhou University, Fuzhou 350116
3.Key Laboratory of Spatial Data Mining and Information Sharing, Ministry of Education, Fuzhou University, Fuzhou 350116
Community detection based on edge clustering is capable of detecting overlapping communities naturally. However, it engenders the problems of obscure belongingness of the nodes on community borders and the excessive overlap of communities. In this paper, an overlapping community detection based on edge density clustering(OCDEDC) algorithm is proposed. Firstly, density clustering based on edges is employed to extract core edge communities. Next, a partitioning strategy is designed to dispatch border edges to its closest core edge community. In addition, a strategy based on the degrees and community belongingness of edges is designed to handle the isolated edges, and thus the excessive overlap of communities is avoided. Finally, edge communities are transformed back into node communities as the output. Experiments on artificial and real datasets show that the proposed algorithm detects overlapping communities efficiently and effectively.
[1] NEWMAN M E J. The Structure of Scientific Collaboration Networks. Proceedings of the National Academy of Sciences of the Uni-ted States of America, 2001, 98(2): 404-409.
[2] FALOUTSOS M, FALOUTSOS P, FALOUTSOS C. On Power-Law Relationships of the Internet Topology. ACM SIGCOMM Computer Communication Review, 1999, 29(4): 251-262.
[3] TAKAFFOLI M, SANGI F, FAGNAN J, et al. Community Evolution Mining in Dynamic Social Networks. Procedia-Social and Behavioral Sciences, 2011, 22: 49-58.
[4] PANAGIOTAKIS C. Community Detection in Graph. Journal of Comparative Physiology A, 1972, 78(4): 346-355.
[5] 王贵参.重叠社区发现中的边聚类算法研究.博士学位论文.长春:吉林大学, 2016.
(WANG G C. Research on Link Clustering Algorithms in Over-lapping Community Detection. Ph.D Dissertation. Changchun, China: Jilin University, 2016.)
[6] PALLA G, DERÉNYI I, FARKAS I, et al. Uncovering the Overlapping Community Structure of Complex Networks in Nature and Society. Nature, 2005, 435: 814-818.
[7] LEE C, REID F, MCDAID A, et al. Detecting Highly Overlapping Community Structure by Greedy Clique Expansion[C/OL]. [2018-02-23]. https://arxiv.org/pdf/1002.1827.pdf.
[8] LANCICHINETTI A, FORTUNATO S, KERTÉSE J. Detecting the Overlapping and Hierarchical Community Structure of Complex Networks. New Journal of Physics, 2009. DOI: 10.1088/1367-2630/11/3/033015.
[9] GREGORY S. Finding Overlapping Communities in Networks by Label Propagation. New Journal of Physics, 2010. DOI: 10.1088/1367-2630/12/10/103018.
[10] EVANS T S, LAMBIOTTE R. Line Graphs, Link Partitions, and Overlapping Communities. Physical Review E, 2009. DOI: 10.1103/PhysRevE.80.016105.
[11] AHN Y Y, BAGROW J P, LEHMANN S. Link Communities Reveal Multiscale Complexity in Networks. Nature, 2010, 466: 761-764.
[12] BALL B, KARRER B, NEWMAN M E J. Efficient and Principled Method for Detecting Communities in Networks. Physical Review E, 2011. DOI: 10.1103/PhysRevE.84.036103.
[13] KIM Y, JEONG H. Map Equation for Link Communities. Physical Review E, 2011. DOI: 10.1103/PhysRevE.84.026110.
[14] 朱 牧,孟凡荣,周 勇.基于链接密度聚类的重叠社区发现算法.计算机研究与发展, 2013, 50(12): 2520-2530.
(ZHU M, MENG F R, ZHOU Y. Density-Based Link Clustering Algorithm for Overlapping Community Detection. Journal of Computer Research and Development, 2013, 50(12): 2520-2530.)
[15] ZHOU X, LIU Y H, WANG J, et al. A Density Based Link Clustering Algorithm for Overlapping Community Detection in Networks. Physica A: Statistical Mechanics and Its Applications, 2017, 486: 65-78.
[16] FORTUNATO S. Community Detection in Graphs. Physics Reports, 2009, 486(3/4/5): 75-174.
[17] LANCICHINETTI A, FORTUNATO S, RADICCHI F. Benchmark Graphs for Testing Community Detection Algorithms. Physical Review E, 2008. DOI: 10.1103/PhysRevE.78.046110.
[18] ZACHARY W W. An Information Flow Model for Conflict and Fi-ssion in Small Groups. Journal of Anthropological Research, 1977, 33(4): 452-473.
[19] LUSSEAU D, SCHNEIDER K, BOISSEAU O J, et al. The Bottlenose Dolphin Community of Doubtful Sound Features a Large Proportion of Long-Lasting Associations. Behavioral Ecology and Sociobiology, 2003, 54(4): 396-405.
[20] NEWMAN M E J, GIRVAN M. Finding and Evaluating Community Structure in Networks. Physical Review E, 2004. DOI: 10.1103/PhysRevE.69.026113.
[21] GIRVAN M, NEWMAN M E J. Community Structure in Social and Biological Networks. Proceeding of the National Academy of Sciences of the United Stated of America, 2002, 99(12): 7821-7826.
[22] ADAMIC L A, GLANCE N. The Political Blogosphere and the 2004 U.S. Election: Divided they Blog // Proc of the 3rd International Workshop on Link Discovery. New York, USA: ACM, 2005: 36-43.
[23] HOLME P, NEWMAN M E J. Nonequilibrium Phase Transition in the Coevolution of Networks and Opinions. Physical Review E, 2006. DOI: 10.1103/PhysRevE.74.056108.
[24] WATTS D J, STROGATZ S H. Collective Dynamics of ′Small-World′ Networks. Nature, 1998, 393(6684): 440-442.
[25] LESKOVEC J, KLEINBERG J, FALOUTSOS C. Graph Evolution: Densification and Shrinking Diameters. ACM Transactions on Knowledge Discovery from Data. 2007. DOI: 10.1145/1217299.1217301.
[26] RAGHAVAN U N, ALBERT R, KUMARA S. Near Linear Time Algorithm to Detect Community Structures in Large-Scale Networks. Physical Review E, 2007. DOI: 10.1103/PhysRevE.76.036106.
[27] DANON L, DÍAZ-GUILERA A, DUCH J, et al. Comparing Community Structure Identification. Journal of Statistical Mechanics: Theory and Experiment, 2005. DOI: 10.1088/1742-5468/2005/09/P09008.
[28] SHEN H W, CHENG X Q, CAI K, et al. Detect Overlapping and Hierarchical Community Structure in Networks. Physica A: Statistical Mechanics & Its Applications, 2008, 388(8): 1706-1712.
2018, Vol. 31 
