ISSN 1000-1239 CN 11-1777/TP

• 人工智能 •

### 基于网络节点中心性度量的重叠社区发现算法

1. 1(山西大学计算机与信息技术学院 太原 030006);2(计算智能与中文信息处理教育部重点实验室(山西大学) 太原 030006) (duhangyuan@sxu.edu.cn)
• 出版日期: 2018-08-01
• 基金资助:
国家自然科学基金项目(61673295,61773247)；山西省自然科学(青年科技研究)基金项目(201701D221097)；山西省回国留学人员科研资助项目(2016-004)；山西省研究生联合培养基地人才培养项目(2017JD05) This work was supported by the National Natural Science Foundation of China (61673295, 61773247), the Natural Science Foundation of Shanxi for Youths (201701D221097), the Research Project Supported by Shanxi Scholarship Council of China (2016-004), and the Program for Fostering Talents of Shanxi Province Joint Postgraduate Training Base (2017JD05).

### An Overlapping Community Detection Algorithm Based on Centrality Measurement of Network Node

Du Hangyuan1, Wang Wenjian2,Bai Liang2

1. 1(College of Computer and Information Technology, Shanxi University, Taiyuan 030006);2(Key Laboratory of Computational Intelligence & Chinese Information Processing(Shanxi University), Ministry of Education, Taiyuan 030006)
• Online: 2018-08-01

Abstract: Based on the idea of density peak clustering method, a centrality measurement model for network nodes is designed, and a new community detection algorithm for overlapping network is also proposed. In the algorithm, the cohesion and separation of network nodes are defined at first, to describe the structural feature of community that the intra links inside one community are dense while the inter links between communities are sparse. Depend on that, centrality measurement is calculated for each node to express its influence on network community structure. Then the nodes with tremendous centralities are selected by the 3δ principle as community centers. The overlapping features between communities are represented by memberships, and the iterative calculation methods for the memberships of non-central nodes are put forward. After that, according to their memberships, all the nodes in network can be allocated to their possible communities to accomplish the overlapping community detection. At last, the proposed algorithm is verified by the simulation on both synthetic networks and social networks. The simulation results reflect that our algorithm outperforms other competitive overlapping community detection algorithms in respect of both detection quality and computational efficiency.