Affiliations: School of Computer and Software Engineering, Xihua University, Chengdu 610039, Sichuan, China
Correspondence:
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Corresponding author: Jia Liu, School of Computer and Software Engineering, Xihua University, Chengdu 610039, Sichuan, China. E-mail: xiaoke92@foxmail.com.
Abstract: The analysis of communities and their evolutionary behaviors in dynamic social networks is a challenging topic. Although a lot of work on this topic is emerging, there are few methods which can reveal and track meaningful communities over time efficiently. In this paper, an approach of social community evolution based on gravitational relationship and community structure in a dynamic social network is proposed. Our proposed method that concentrates on both community structure and micro-blog content can be used to reveal and track the process of community evolution. First, we set up the undirected dynamic network in the original snapshot. Second, communities are initialized by constructing the basic nodes, and the other nodes can be iteratively searched and divided into corresponding communities. Third, we analyze community evolution and propose DCVIE algorithm to extract community evolution behavior. Finally, The experimental results show that the proposed method performs well on detecting communities as well as tracking communities evolution in dynamic social networks.
Keywords: Community detection, community evolution, gravitational relationship, community structure
