基于密度聚类的增量动态社区发现算法

doi:10.16451/j.cnki.issn1003-6059.201811001

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摘要现实世界中社交网络中的节点和边随时间动态增加或消失,导致网络中的社区结构也随之发生变化,因此,文中提出基于密度聚类的增量动态社区发现算法.首先,基于改进后的DBSCAN生成初始时刻社区.然后,提出边变化率指标,并结合余弦相似度指标确定相邻时刻邻居发生变化的节点及其邻居节点的社区归属调整.在进行社区归属度计算时,不仅考虑节点直接邻居的影响,还考虑间接邻居的影响.最后,通过迭代更新模块度增益进行社区合并,以减少噪声社区的干扰.在人工数据集和真实数据集上的实验表明,文中算法可以有效应对网络结构突变和增量计算累积误差带来的影响,具有较低的时间复杂度.

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	郭昆
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关键词 ：动态社区发现, 密度聚类, 边变化率, 模块度

Abstract：The community structures of social networks in the real world are always varying with nodes and edges of social networks increasing or disappearing dynamically as time goes by. In this paper, an incremental dynamic community detection algorithm based on density clustering is proposed. Firstly, the initial communities are generated according to the improved DBSCAN algorithm. Then, an index of edge variation rate is proposed and it is combined with the cosine similarity index to determine the community belonging adjustment process of the nodes whose neighbors vary in adjacent moment. In addition, both direct and indirect neighbor nodes are taken into account during the calculation of community belongingness.Finally, the communities are merged by iteratively updating the modularity gain to reduce the interference of noise communities. Experimental results on artificial datasets and real networks show that the proposed algorithm effectively copes with the variation of the network structures and incremental calculation cumulative errors with a low time complexity.

Key words： Dynamic Community Detection Density Clustering Edge Variation Rate Modularity

收稿日期: 2018-05-31

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(No.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网络、计算智能算法、VLSI 物理设计算法.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、灰色系统理论、数据挖掘.E-mail:gukn@fzu.edu.cn;彭胜波,硕士研究生,主要研究方向为复杂网络、数据挖掘.E-mail:pengsb0308@126.com;陈羽中,博士,教授,主要研究方向为复杂网络、计算智能、数据挖掘.E-mail:yzchen@163.com；

引用本文:

郭昆, 彭胜波, 陈羽中, 郭文忠. 基于密度聚类的增量动态社区发现算法[J]. 模式识别与人工智能, 2018, 31(11): 965-978. GUO Kun, PENG Shengbo, CHEN Yuzhong, GUO Wenzhong. Incremental Dynamic Community Detection Algorithm Based on Density Clustering. , 2018, 31(11): 965-978.

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http://manu46.magtech.com.cn/Jweb_prai/CN/10.16451/j.cnki.issn1003-6059.201811001 或 http://manu46.magtech.com.cn/Jweb_prai/CN/Y2018/V31/I11/965

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