%A WANG Xin, ZUO Wan-li, ZHU Feng-tong, WANG Ying %T Important-node-based community detection algorithm %0 Journal Article %D 2018 %J JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) %R 10.6040/j.issn.1671-9352.1.2017.023 %P 67-77 %V 53 %N 11 %U {http://lxbwk.njournal.sdu.edu.cn/CN/abstract/article_2993.shtml} %8 %X The internal community structure is the specific expression of structural features and attribute features in complex networks. First, the maximum k eigenvectors matrix of the modularity matrix of the network was calculated based on the modularity maximization theory. Then, the method of cluster centrality was proposed and used to calculate the important nodes of the k communities as k cluster centralities. The distance between each node and k cluster centralities was calculate by Euclidean distance and each node was assigned to the nearest cluster centrality of the community. Final, k-means iterative calculation method was applied into the network and the k communities divisions was eventually obtained. The algorithm was verified experimentally on both Karate Club Network dataset and American College Football dataset. The experimental results show that the algorithm can effectively identify potential community. The algorithm improves the purity and modularity to a certain extent compared with other community detection algorithms, and with fewer iterations and higher efficiency.