Computer Engineering and Applications ›› 2017, Vol. 53 ›› Issue (15): 164-169.DOI: 10.3778/j.issn.1002-8331.1611-0487

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Community mining algorithm based on central maximal-clique expansion

ZHAO Weiji1,2, ZHANG Fengbin2, LIU Jinglian1, JIN Hao1   

  1. 1.School of Information Engineering, Suihua University, Suihua, Heilongjiang 152061, China
    2.School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China
  • Online:2017-08-01 Published:2017-08-14


赵卫绩1,2,张凤斌2,刘井莲1,金  昊1   

  1. 1.绥化学院 信息工程学院,黑龙江 绥化 152061
    2.哈尔滨理工大学 计算机科学与技术学院,哈尔滨 150080


Community mining is an important work in complex network analysis, and many algorithms have been proposed. However, most of them are based on the links to find the cohesive community structure. Taking the nodes that have different behaviors and influences in real-world networks into consideration, together with links between nodes, a two?stage community mining algorithm based on central maximal-clique expansion is proposed. In the first stage, initial communities are found: Firstly, all the cohesive cliques are found out in the network, and then [k] separate cohesive and influential central maximal-cliques are chosen to form initial communities. In the second stage, the final community division is detected: For the nodes outside the initial communities, taking potential impacts of neighbor nodes into consideration, the neighbor nodes are expanded to the corresponding connected closely community by adopting the local modularity. Experimental results show that the method can quickly reveal cohesive community structure in network, compared with the FN algorithm it has a relatively higher accuracy and modularity, compared with the GN algorithm, it do not need to know the prior number of communities.

Key words: community structure, central maximal-clique, local modularity



关键词: 社区结构, 中心极大团, 局部模块度