Distance dynamics based overlapping semantic community detection for node‐attributed networks. (14th April 2020)

• Record Type: Journal Article
• Authors: Sun, Heli; Jia, Xiaolin; Huang, Ruodan; Wang, Pei; Wang, Chenyu; Huang, Jianbin
• Other Names: Li Ying guestEditor.; Shyamasundar R.K. guestEditor.; Wang Xinheng guestEditor.
• Is Part Of: Computational intelligence. Volume 37:Number 2(2021)
• Page Start: 678
• Page End: 700
• Publication Date: 2020-04-14
• Subjects: community detection -- distance dynamics -- interaction model -- node‐attributed network -- semantic community; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12324
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: In recent years, due to the rise of social, biological, and other rich content graphs, several novel community detection methods using structure and node attributes have been proposed. Moreover, nodes in a network are naturally characterized by multiple community memberships and there is growing interest in overlapping community detection algorithms. In this paper, we design a weighted vertex interaction model based on distance dynamics to divide the network, furthermore, we propose a distance Dynamics‐based Overlapping Semantic Community detection algorithm(DOSC) for node‐attribute networks. The method is divided into three phases: Firstly, we detect local single‐attribute subcommunities in each attribute‐induced graph based on the weighted vertex interaction model. Then, a hypergraph is constructed by using the subcommunities obtained in the previous step. Finally, the weighted vertex interaction model is used in the hypergraph to get global semantic communities. Experimental results in real‐world networks demonstrate that DOSC is a more effective semantic community detection method compared with state‐of‐the‐art methods.