Research of Dynamic Community Discovery Based on Role Assorted Thoughts
基于角色划分的动态社区挖掘算法研究

Traditional community discovery algorithms focus on the analysis of static topology structure of networks while ignoring the influence of individual activity on the formation of networks. This paper introduced the concept of community seed and liaison, and aiming at the special nodes, researched and analyzed the formation and evolution mecha- nism of social network from both individualism and structuralism perspectives, proposed a role assorted community dis- cowry algorithm. This paper tested the performance of this algorithm both on artificial network and real-world net- works and compared the results with GN, fast GN and Polish. Experimental results show that the results of role as- sorted algorithm are much better than GN algorithm, with great suitability and expandability. Besides, the discovery communities arc all strong connected communities.