Detection Algorithm Based on Closeness Rank and Signal Transimission
In the algorithm of community detection using clustering technology,the prior information of community structure and the similarity measure between nodes influence the clustering effect greatly.For how to adaptively discover the community and utilize the network topology to calculate the similarity between nodes in order to raise the modularity of network,the community detection algorithm based on closeness ranking and signaling transmission has been proposed in this paper.The main idea of the algorithm is to rank the closeness centrality of nodes,select the initial center nodes according to the certain rules.The similarity between nodes are calculated according to the idea of signal transmission,and each node in the community is assigned to the candidate community.Finally,Merging of small community are carried out adaptively,and the center nodes set are updated iteratively until the community is stable.Experiments are carried out on Zachary Karate Club Network,American College Football,Dolphin Social Network,Lesmis vocabulary network,and the results demonstrate the feasibility and efficiency of the proposed algorithm.
complex network community detection closeness centrality signal transmission community modularity
Lv Hui Lei Tao Huang Xianglin Gao Hongxiao Bi zengfeng
Tian Yun Rongchuang Data Technology(Beijing)Co.Ltd.;Faculty of Science and Technology,Communication Tian Yun Rongchuang Data Technology(Beijing)Co.Ltd. Faculty of Science and Technology,Communication University of China Beijing Jinshuiyanyu Technology Co.Ltd.Beijing,China
国际会议
重庆
英文
443-447
2017-03-25（万方平台首次上网日期，不代表论文的发表时间）