基于边密度聚类的重叠社区发现算法

doi:10.16451/j.cnki.issn1003-6059.201808002

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摘要

基于边聚类的社区发现算法以边为聚类对象,自然发现重叠社区,但也存在生成的社区集边界归属模糊、社区结构过度重叠等问题.基于此种情况,文中提出基于边密度聚类的重叠社区发现算法.首先,以边为研究对象,通过密度聚类检测连接紧密的核心边社区.然后,根据边界边归属策略将边界边划分到离它最近的核心边社区.针对孤立边,提出基于边的度与边的社区归属的孤立边处理策略,进一步处理未划分的孤立边,避免社区结构过度重叠的问题.最后,将边社区还原为节点社区,实现重叠社区的发现.在人工数据集和真实数据集上的实验表明,文中算法可以快速准确地检测复杂网络中的重叠社区.

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	郭昆
	陈而宝
	郭文忠

关键词 ：重叠社区, 复杂网络, 边聚类, 密度聚类

Abstract：

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.

Key words： Overlapping Community Complex Network Edge Clustering Density Clustering

收稿日期: 2018-04-03

ZTFLH:

TP 391

基金资助:

国家自然科学基金项目(No.61300104,61300103,61672158)、福建省自然科学基金项目(No. 2013J01230,2014J01232)、福建省高校杰出青年科学基金项目(No.JA12016)、福建省高等学校新世纪优秀人才支持计划项目(No.JA13021)、福建省杰出青年科学基金项目(No.2014J06017,2015J06014)、福建省科技创新平台计划项目(2009J1007,2014H2005)、福建省高校产学合作项目(No.2014H6014,2017H6008)、海西政务大数据应用协同创新中心资助

通讯作者: 郭文忠,博士,教授,主要研究方向为计算机智能及其应用.E-mail:guowenzhong@fzu.edu.cn.

作者简介: 郭昆,博士,副教授,主要研究方向为复杂网络分析、数据挖掘等.E-mail:gukn123@163.com. 陈而宝,硕士研究生,主要研究方向为社区发现.E-mail:ceb112510@163.com.

引用本文:

郭昆, 陈而宝, 郭文忠. 基于边密度聚类的重叠社区发现算法[J]. 模式识别与人工智能, 2018, 31(8): 693-703. GUO Kun, CHEN Erbao, GUO Wenzhong. Overlapping Community Detection Based on Edge Density Clustering. , 2018, 31(8): 693-703.

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http://manu46.magtech.com.cn/Jweb_prai/CN/10.16451/j.cnki.issn1003-6059.201808002 或 http://manu46.magtech.com.cn/Jweb_prai/CN/Y2018/V31/I8/693

[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.