Journal of Computer Applications ›› 2019, Vol. 39 ›› Issue (7): 2024-2029.DOI: 10.11772/j.issn.1001-9081.2019010183

• Network and communications • Previous Articles     Next Articles

Community dividing algorithm based on similarity of common neighbor nodes

FU Lidong<sup>1,2</sup>, HAO Wei<sup>1</sup>, LI Dan<sup>1</sup>, LI Fan<sup>1</sup>   

  1. 1. College of Computer Science and Technology, Xi'an University of Science and Technology, Xi'an Shaanxi 710054, China;
    2. School of Computer Science and Technology, Xidian University, Xi'an Shaanxi 710071, China
  • Received:2019-01-24 Revised:2019-03-13 Online:2019-04-15 Published:2019-07-10
  • Supported by:

    This work is partially supported by the National Natural Science Foundation of China (61432010, 61502363), the Postdoctoral Research Start-up Project of Xi'an University of Science and Technology (2018QDJ049).


付立东1,2, 郝伟1, 李丹1, 李凡1   

  1. 1. 西安科技大学 计算机科学与技术学院, 西安 710054;
    2. 西安电子科技大学 计算机科学与技术学院, 西安 710071
  • 通讯作者: 付立东
  • 作者简介:付立东(1973-),男,陕西西安人,副教授,博士,CCF会员,主要研究方向:复杂网络计算;郝伟(1995-),男,陕西西安人,硕士研究生,主要研究方向:复杂网络计算;李丹(1994-),女,陕西宝鸡人,硕士研究生,主要研究方向:复杂网络计算;李凡(1995-),女,陕西商洛人,硕士研究生,主要研究方向:复杂网络计算。
  • 基金资助:



The community structure in complex networks can help people recognize basic structure and functions of network. Aiming at the problems of low accuracy and high complexity of most community division algorithms, a community division algorithm based on similarity of common neighbor nodes was proposed. Firstly, a similarity model was proposed in order to calculate the similarity between nodes. In the model, the accuracy of similarity measurement was improved by calculating the tested node pairs and their neighbor nodes together. Secondly, local influence values of nodes were calculated, objectively showing the importances of nodes in the network. Thirdly, the nodes were hierarchically clustered according to the similarity and local influence values of nodes, and preliminary division of network community structure was completed. Finally, the preliminary divided sub-communities were clustered until the optimal modularity value was obtained. The simulation results show that compared with the new Community Detection Algorithm based on Local Similarity (CDALS), the proposed algorithm has the accuuracy improved by 14%, which proves that the proposed algorithm can divide the community structure of complex networks accurately and effectively.

Key words: common neighbor node, similarity measurement, local influence of node, modularity, community division



关键词: 共邻节点, 相似度度量, 节点局部影响力, 模块度, 社区划分

CLC Number: