To improve the accuracy of the community detection algorithm to identify the center nodes and the robustness of the algorithm,a local collaborative optimization based on the effective degree for community detection algorithm(LCED)was pro-posed.The effective degree index was designed,and the community structure was naturally divided by multiple local expansions.In the process of local expansion,the attraction function of node pairs was given as the basis of node access order,which im-proved the robustness of the algorithm,and the adaptation function was improved to solve the over-fusion problem.The conflict nodes were assigned to optimize the community structure.Experimental results of comparing algorithms on real and synthetic networks show that the LCED algorithm has higher accuracy and robustness.
关键词
社区发现/有效度/吸引力函数/适应度函数/社交网络/社区结构/社区中心
Key words
community detection/effective degree/fitness function/attraction function/social networks/community structure/community centers
维普
万方数据