吉林大学学报(理学版)

• 计算机科学 • 上一篇    下一篇

基于拓扑势的局部化重叠社区识别

张桂杰1,2, 张健沛2, 杨静2, 王帅1   

  1. 1. 吉林师范大学 计算机科学与技术学院, 吉林 四平 136000; 2. 哈尔滨工程大学 计算机科学与技术学院, 哈尔滨 150001
  • 收稿日期:2014-08-06 出版日期:2015-07-26 发布日期:2015-07-27
  • 通讯作者: 张桂杰 E-mail:zhangguijie@hrbeu.edu.cn

Uncovering Overlapping Communities by Local\=Similarity Based on Topological Potential

ZHANG Guijie1,2, ZHANG Jianpei2, YANG Jing2, WANG Shuai1   

  1. 1. College of Computer Science and Technology, Jilin Normal University, Siping 136000, Jilin Province, China;2. College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China
  • Received:2014-08-06 Online:2015-07-26 Published:2015-07-27
  • Contact: ZHANG Guijie E-mail:zhangguijie@hrbeu.edu.cn

摘要:

针对传统社区识别算法中需要根据先验知识设定参数、 社区划分结果具有随机性及复杂度过高的问题, 提出一种基于拓扑势的局部化重叠社区识别算法. 该算法通过引入拓扑势计算节点的影响力, 利用节点间的局部相似性度量指标, 采用标签传播策略进行重叠结构的社区识别. 在真实网络及人工合成网络上与多种经典算法进行对比实验验证了算法的高效性.

关键词: 社区结构, 拓扑势, 局部相似度, 标签传播, 重叠社区社区结构, 拓扑势, 局部相似度, 标签传播, 重叠社区

Abstract:

We proposed a local community detection algorithm based on topological potential, which uses topological potential of nodes to calculate their influence, and then takes the strategy of label propagation algorithm to detect overlap community structures via a new measurement index based on the similarity of local structures. The algorithm solves the problems of parameter setting, random result and high complexity of traditional algorithms. Algorithm comparison experiments on real world and computer generated datasets show that it is efficient.

Key words: community structure, topological potential, local similarity, label propagation, overlapping community

中图分类号: 

  • TP391