ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2015, Vol. 52 ›› Issue (10): 2363-2372.doi: 10.7544/issn1000-1239.2015.20150407

所属专题: 2015网络安全与隐私保护研究进展

• 信息安全 • 上一篇    下一篇

基于节点亲密度和度的社会网络社团发现方法

刘瑶,康晓慧,高红,刘峤,吴祖峰,秦志光   

  1. (电子科技大学信息与软件工程学院 成都 610054) (liuyao@uestc.edu.cn)
  • 出版日期: 2015-10-01
  • 基金资助: 
    基金项目:国家自然科学基金重点项目(61133016);中央高校基本科研业务费基础研究项目(ZYGX2014J066);国家自然科学基金青年项目(61502087)

A Community Detecting Method Based on the Node Intimacy and Degree in Social Network

Liu Yao, Kang Xiaohui, Gao Hong, Liu Qiao, Wu Zufeng, Qin Zhiguang   

  1. (School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054)
  • Online: 2015-10-01

摘要: 社会网络是现实社会在网络空间的延伸,研究社会网络的结构特征对于发现网络结构、预测网络行为、保障网络安全有着重要的意义.社团结构是社会网络最重要的一种结构特征.近年来,研究人员提出了大量的社团检测算法,但大多集中在无权网络,不能处理网络中越来越复杂的连接关系.为了衡量有向加权网络中节点之间的关联强度,提出了一种新的节点亲密度定义,在此基础上设计了一种基于节点亲密度和度的社团结构检测方法(community detecting method based on node intimacy and degree, CDID),并在真实的社会网络数据集上进行了实验验证.与传统的社团检测方法相比,CDID方法能够获得更加准确的社团划分结果,并为无向无权、有向无权、无向加权、有向加权网络的社团划分提供了一种统一的解决方法.

关键词: 节点亲密度, 节点度, 加权网络, 模块度, 社团检测

Abstract: Social network is an extension of realistic society in cyberspace. The research on structural characteristics of social network has an important significance on network architecture discovery, network behavior forecast and network security protection. The community structure is one of the basic and important structural characteristics of social network. In recent years, a lot of algorithms for community detecting in social network have been proposed. But they always focuse on unweighted networks, and can’t handle the more and more complex connect relationships between nodes. In order to measure the connection strength in directed and weighted networks, a new definition of node intimacy is proposed. Then, a community detecting method based on node intimacy and degree (CDID) is designed. This method is verified through a series of experiments on synthetic datasets and real-world social network datasets. Compared with other state-of-the-art algorithms, this methed can obtain more accurate community division results under a reasonable run time. And it also provides a unification community detecting method for the four different type networks, such as undirected-unweighted, directed-unweighted, undirected-weighted and directed-weighted networks.

Key words: node intimacy, node degree, weighted networks, modularity, community detecting

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