Community Detection Based on Local Information and Dynamic Expansion

作     者：Luo, Yongping Wang, Li Sun, Shiwen Xia, Chengyi 

作者机构：Tianjin Univ Technol Key Lab Intelligence Comp & Novel Software Techno Tianjin 300384 Peoples R China Minist Educ Key Lab Comp Vis & Syst Tianjin 300384 Peoples R China 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2019年第7卷

页      面：142773-142786页

核心收录：

基　　金：National Natural Science Foundation of China (NSFC) [61773286  61374169  61403280] 

主　　题：Heuristic algorithms Detection algorithms Clustering algorithms Image edge detection Complex networks Social network services Symmetric matrices Community detection global community local modularity local information local community 

摘      要：Mining the community structure in the real-world networks has been a hot topic in the field of complex networks, and has emerged as a prominent research area and continues to grow with the introduction of requirements for personalized recommendation. However, most of the existing community detection algorithms are based on global information, fewer works are devoted to detecting the communities hidden in the network by using local information. To this end, in this paper, we propose two improved signed modularity functions to evaluate the community properties in complex network, and then we apply these indicators to identify the community structure by using the local information since it is difficult to obtain the global information in practice. During the dynamic expansion, each local community will absorb the neighboring node with the highest positive energy;in addition, a new local community is generated when all local communities cannot contain the neighboring node. Finally, the algorithm has been applied to unsigned networks and signed networks, respectively. The experimental results show that the division results given by our proposed algorithm are in line with the actual ones in artificial and real-life networks.