[1]张月霞,杨瑞琪,康劲.基于弱派系的多层社会网络重叠社团发现算法[J].深圳大学学报理工版,2018,35(4):413-419.[doi:10.3724/SP.J.1249.2018.04413]
 ZHANG Yuexia,YANG Ruiqi,and KANG Jin.Overlapping community detection algorithm based on weak clique in multi-layer social networks[J].Journal of Shenzhen University Science and Engineering,2018,35(4):413-419.[doi:10.3724/SP.J.1249.2018.04413]
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基于弱派系的多层社会网络重叠社团发现算法()
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《深圳大学学报理工版》[ISSN:1000-2618/CN:44-1401/N]

卷:
第35卷
期数:
2018年第4期
页码:
413-419
栏目:
电子与信息科学
出版日期:
2018-07-10

文章信息/Info

Title:
Overlapping community detection algorithm based on weak clique in multi-layer social networks
作者:
张月霞杨瑞琪康劲
北京信息科技大学信息与通信工程学院, 北京 100101
Author(s):
ZHANG Yuexia YANG Ruiqi and KANG Jin
School of Information and Communication Engineering, Beijing Information Science & Technology University, Beijing 100101
关键词:
计算机网络多层社会网络弱派系重叠社团社团发现
Keywords:
computer network multi-layer social network weak clique overlapping community community detection
分类号:
TP 393
DOI:
10.3724/SP.J.1249.2018.04413
文献标志码:
A
摘要:
针对现有社团发现算法中关于多层社会网络的重叠社团发现算法较少,且较难检测小型多层网络中社团的问题,提出一种基于弱派系的多层社会网络重叠社团发现算法.算法通过检测与合并网络中的弱派系得到社团发现结果,弱派系的构建综合考虑了节点度和节点邻居间的连接,得到更细粒度的社团结构,并同时适用于无向与有向网络.真实网络的实验结果表明,该算法可有效检测小型多层社会网络中的重叠社团,优于现有的基于局部社团的社团发现算法(local community based community detection algorithm,LC-CDA算法).
Abstract:
There are few overlapping community detection algorithms in the existing community detection algorithms and it’s difficult to detect communities in small multi-layer social networks. To solve above problems, we propose an overlapping community detection algorithm based on weak clique in multi-layer social works. Our new algorithm detects communities by detecting and merging weak cliques in the network. The construction of weak clique considers the node degree and the number of links between the neighbor nodes. This method can obtain finer-grained community structures and is suitable for both undirected and directed networks. The results of experiments tested in real world networks show that this method can detect overlapping community in small multi-layer social networks effectively and is superior to the local community based community detection algorithm (LC-CDA).

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更新日期/Last Update: 2018-06-20