• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

计算机工程与科学

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基于链接寿命的社交网络结构演化分析

梁勤1,李磊1,刘冠峰2   

  1. (1.合肥工业大学计算机与信息学院,安徽 合肥 230009;
    2.苏州大学苏州先进数据分析实验室,江苏 苏州 215006)
  • 收稿日期:2015-08-31 修回日期:2015-10-29 出版日期:2016-10-25 发布日期:2016-10-25
  • 基金资助:

    国家自然科学基金(61503114);安徽省自然科学基金(1408085QF130);中央高校基本科研业务费专项资金(2015HGCH0012).

Link life based social network structure evolution analysis

LIANG Qin1,LI Lei1,LIU Guan-feng2   

  1. (1.School of Computer Science and Information Technology,Hefei University of Technology,Hefei 230009;
    2.Soochow Advanced Data Analytics Lab,Soochow University,Suzhou 215006,China)
  • Received:2015-08-31 Revised:2015-10-29 Online:2016-10-25 Published:2016-10-25

摘要:

近些年来,社交网络受到越来越多的关注。社会网络服务(SNS),例如YouTube、Facebook和Twitter等,已经成为网络上最受欢迎的网络应用之一。SNS的风靡促使越来越多的人研究社交网络的特性,特别是基于网络拓扑结构的研究,以期改善当前的网络应用并创造新的受欢迎的社交网络应用。然而,大多数的现有研究方法只是研究随着时间积累的网络结构的动态变化,这些方法无法完全反映社交网络的其他特性比如链接寿命现象。链接寿命现象是指社交网络中的边并不是永久存在的,它可能会随着时间的变化而消亡。着重研究这种社交网络中链接生存周期对社交网络结构演化的影响。具体来说,研究链接寿命对于社交网络结构基础重要参数(包括度、网络直径和平均聚类系数等)的影响。基于DBLP的真实网络数据的研究表明,在考虑链接寿命这个必要因素之后,社交网络结构的演化结果和传统研究结果有很大的不同。特别是,链接寿命的微小变化会导致网络直径的剧烈变化。
 

关键词: 社交网络, 拓扑结构, 链接寿命

Abstract:

Social networks have got increasing attention in recent years, and social networking services (SNSs), such as YouTube, Facebook and Twitter, are among the most popular network applications on the internet. The popularity of these SNSs attracts more and more researchers to focus on the study of the characteristics of the social network, especially the topology of networks, so as to improve current systems and to design new applications of social networks. However, most existing studies only focus on the dynamical network structure changing with time accumulation, and they do not take into account of other properties of the social network structure, such as link life. The link life reflects the phenomenon that the link established in the past may not be permanent and may vanish as time goes by. In this paper, we focus on the influence of link life on the dynamical network structure evolution, more specifically, on the basic and important parameters of the social network structure, including degree, diameter and average clustering coefficients. Experiments on the real datasets from the DBLP shows that the link life is necessary and important, which makes a great difference to the social network structure evolution. Particularly, the trivial perturbation of link life can lead to a dramatic change of the network diameter.

Key words: social network, topology structure, link life