Algorithm for New Dynamic Social Interaction Network Recognition Based on Depth Search First Strategy
This paper proposed a framework and an algorithm for identifying communities in dynamic social networks. In order to handle the drawbacks of traditional approaches for social network analysis, we utilize the community similarities and infrequent change of community members combined with community structure optimization to develop a Group-based social community identification model to analyze the change of social interaction network with multiple time steps. According to this model, we introduced a greed-cut algorithm and depth-search-first approach and combine them to develop a new algorithm for dynamic social interaction network recognition (called ADSIN). In addition, we conduct experiments on the dataset of Southern Women, the experiment results validate the accuracy and effectiveness of ADSIN.
social network community recognition time step connectivity graph
Gang Wang Yuli Lei Jiliu Zhou Chongjun Wang Shaqjie Qiao
College of Computer Science ,Sichuan University, Chengdu Sichuan, 610065, China Department of Comput National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing Jiangsu, 210093, College of Computer Science ,Sichuan University, Chengdu Sichuan. 610065, China School of Information Science and Technology, Southwest Jiaotong University, Chengdu Sichuan, 610031
国际会议
太原
英文
644-647
2011-02-26（万方平台首次上网日期，不代表论文的发表时间）