›› 2015,Vol. 30 ›› Issue (1): 184-199.doi: 10.1007/s11390-015-1512-7

所属专题： 不能删除； Data Management and Data Mining

• Special Section on Selected Paper from NPC 2011 • 上一篇    下一篇

一个三层次的在线网络社交影响研究综述

Hui Li(李辉), Member, CCF, ACM, Jiang-Tao Cui(崔江涛), Member, CCF, ACM, Jian-Feng Ma(马建峰), Member, CCF, IEEE   

  1. School of Cyber Engineering, Xidian University, Xi'an 710126, China
  • 收稿日期:2014-01-10 修回日期:2014-04-25 出版日期:2015-01-05 发布日期:2015-01-05
  • 作者简介:Hui Li received his B.E. degree from Harbin Institute of Technology in 2005 and Ph.D. degree in computer engineering from Nanyang Techno- logical University, Singapore, in July 2012. He is an Associate Professor in the School of Cyber Engineering, Xidian University, Xi'an. His research interests include data mining, knowledge management and discovery, privacy-preserving query and analysis in big data. He is a member of CCF and ACM.
  • 基金资助:

    This work was supported by the National Natural Science Foundation of China under Grant Nos. 61173089, 61202179, 61472298, and U1135002, the Scientific Research Foundation for the Returned Overseas Chinese Scholars of State Education Ministry of China, and the Fundamental Research Funds for the Central Universities of China.

Social Influence Study in Online Networks: A Three-Level Review

Hui Li(李辉), Member, CCF, ACM, Jiang-Tao Cui(崔江涛), Member, CCF, ACM, Jian-Feng Ma(马建峰), Member, CCF, IEEE   

  1. School of Cyber Engineering, Xidian University, Xi'an 710126, China
  • Received:2014-01-10 Revised:2014-04-25 Online:2015-01-05 Published:2015-01-05
  • About author:Hui Li received his B.E. degree from Harbin Institute of Technology in 2005 and Ph.D. degree in computer engineering from Nanyang Techno- logical University, Singapore, in July 2012. He is an Associate Professor in the School of Cyber Engineering, Xidian University, Xi'an. His research interests include data mining, knowledge management and discovery, privacy-preserving query and analysis in big data. He is a member of CCF and ACM.
  • Supported by:

    This work was supported by the National Natural Science Foundation of China under Grant Nos. 61173089, 61202179, 61472298, and U1135002, the Scientific Research Foundation for the Returned Overseas Chinese Scholars of State Education Ministry of China, and the Fundamental Research Funds for the Central Universities of China.

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社交网络分析将社会关系用图论的方式转变为节点和边来研究:用节点表示社交网络中的每个个体;边表示个体间的关系,包括朋友、通讯、信任等.这些关系受个体间的社交影响力驱动,这也正是社交网络区别于其他网络的最本质原因.在本文中,我们对驱动社交网络的社交影响力研究领域的研究成果进行了总结.本文将这些成果归纳为三层模型,即个体、社团和网络.通过本文对现有算法和模型的总结,我们可以揭示社交网络研究及其一系列应用的未来发展方向.

Abstract: Social network analysis (SNA) views social relationships in terms of network theory consisting of nodes and ties. Nodes are the individual actors within the networks; ties are the relationships between the actors. In the sequel, we will use the term node and individual interchangeably. The relationship could be friendship, communication, trust, etc. These reason is that these relationships and ties are driven by social in uence, which is the most important phenomenon that distinguishes social network from other networks. In this paper, we present an overview of the representative research work in social in uence study. Those studies can be classi ed into three levels, namely individual, community, and network levels. Throughout the study, we are able to unveil a series of research directions in future and possible applications based on the state-of-the-art study.

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