Detecting Highly Overlapping Community Structure Based on Maximal Clique Networks

被引:0
|
作者
Wu, Peng [1 ]
Pan, Li [1 ]
机构
[1] Shanghai Jiao Tong Univ, Natl Engn Lab Informat Content Anal Technol, Sch Elect Informat & Elect Engn, Shanghai 200240, Peoples R China
来源
2014 PROCEEDINGS OF THE IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM 2014) | 2014年
关键词
social networks; overlapping community detection; label propagation; maximal cliques;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Most of overlapping community detection algorithms cannot be applied to networks with highly overlapping community such as online social networks where individuals belong to many communities. One important reason is that many algorithms detect communities based on the explicit borders where nodes have more connections inside the communities, however, when the vertices' membership number gets large, the explicit borders between communities will fade away. To overcome this disadvantage, a new algorithm named MCNLPA is proposed by expanding the traditional Label Propagation Algorithm (LPA) based on the Maximal Clique Network for highly overlapping community detection. By finding all maximal cliques in networks and defining reasonable edges between them, the maximal clique network is established. Then the updated rule of classic LPA is modified to apply to the maximal network. Experiments show that MCNLPA has a relatively good performance in highly overlapping community detection and overlapping nodes identification.
引用
收藏
页码:196 / 199
页数:4
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