[1]郑文萍,张浩杰,王杰.基于稠密子图的社区发现算法[J].智能系统学报编辑部,2016,(3):426-432.[doi:10.11992/tis.201603045]
 ZHENG Wenping,ZHANG Haojie,WANG Jie.Community detection algorithm based on dense subgraphs[J].CAAI Transactions on Intelligent Systems,2016,(3):426-432.[doi:10.11992/tis.201603045]
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《智能系统学报》编辑部[ISSN:1673-4785/CN:23-1538/TP]

卷:
期数:
2016年3期
页码:
426-432
栏目:
出版日期:
2016-06-25

文章信息/Info

Title:
Community detection algorithm based on dense subgraphs
作者:
郑文萍12 张浩杰1 王杰12
1. 山西大学 计算机与信息技术学院, 山西 太原 030006;
2. 山西大学 计算智能与中文信息处理教育部重点实验室, 山西 太原 030006
Author(s):
ZHENG Wenping12 ZHANG Haojie1 WANG Jie12
1. School of Computer and Information Technology, Shanxi University, Taiyuan 030006, China;
2. Key Laboratory of Computation Intelligence and Chinese Information Processing, Ministry of Education, Shanxi University, Taiyuan 030006, China
关键词:
复杂网络社区发现图聚类软聚类密度中心扩展策略点介数模块性
Keywords:
complex networkcommunity detectiongraph clusteringsoft clusteringdensitycore extended strategyvertex betweennessmodularity
分类号:
TP18
DOI:
10.11992/tis.201603045
摘要:
基于密度的图聚类算法在社区发现中得到了广泛应用,然而由于其通过搜索网络中局部稠密子图来识别社区,使得大量结点因不能构成稠密子图而未被聚类。针对此问题,给出了一种基于稠密子图的软聚类算法(community detection based dense subgraphs,BDSG)。首先给出一种中心社区发现方法;进而定义了一种结点的社区归属度,并给出中心社区扩展策略;最终得到聚类结果。通过与CPM(clique percolation method)、k-dense算法在空手道俱乐部、海豚社交网络、大学生足球网络、电子邮件网络和合作网络等数据进行比较,表明BDSG算法在模块性指标与时间效率方面体现了良好性能, 同时中心社区扩展策略能在一定程度上提高CPM、k-dense等基于密度算法的聚类有效性。
Abstract:
The density-based graph clustering algorithm has been widely used in community detection. However, because it identifies a community by searching a partially dense subgraph in the network, many nodes do not constitute a dense subgraph and are therefore difficult to cluster. In this paper, we present a soft clustering algorithm based on dense subgraphs (BDSG) for detecting communities in complex networks. First, we propose a method for detecting the central communities. Next, we define the degree of community attribution of a node, and put forward a core community extended strategy. Finally, we obtain the clustering results of a network. Compared with the clique percolation method (CPM), k-dense algorithms from Zachary’s Karate Club, the dolphin social network, the American college football network, the email network, and the collaboration network, BDSG shows considerably better performance with respect to modularity and time efficiency. In addition, the proposed core community extended strategy may improve the effectiveness of the clustering-methods-based density, such as that in CPM, k-dense algorithms, and others.

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备注/Memo

备注/Memo:
收稿日期:2016-3-19;改回日期:。
基金项目:国家自然科学基金项目(61572005,61272004),山西省煤基重点科技攻关项目(MQ2014-09).
作者简介:郑文萍,1979年生,女,副教授,中国计算机学会会员,主要研究方向为图论算法、生物信息学等。主持多项国家级项目,发表学术论文多篇。张浩杰,1991年8月生,男,硕士研究生,主要研究方向为数据挖掘、图聚类等。王杰,1988年8月生,男,博士研究生,主要研究方向为数据挖掘,生物信息学。
通讯作者:郑文萍.E-mail:wpzheng@sxu.edu.cn.
更新日期/Last Update: 1900-01-01