引用本文:韩忠明,陈炎,刘雯,原碧鸿,李梦琪,段大高.社会网络节点影响力分析研究.软件学报,2017,28(1):84-104
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社会网络节点影响力分析研究
韩忠明1,2, 陈炎1, 刘雯1, 原碧鸿1, 李梦琪1, 段大高1
1.北京工商大学计算机与信息工程学院, 北京 100048;2.食品安全大数据技术北京市重点实验室, 北京 100048
摘要:
社会网络节点影响力研究是社会网络分析的关键问题之一.过去的10多年间,随着在线社会网络的快速发展,研究人员有机会在大量现实社会网络上对影响力进行分析和建模,并取得了丰硕的研究成果和广泛的应用价值.分析和总结了近年来社会网络影响力分析的主要成果.首先介绍了节点影响力的相关定义、作用范围以及表现形式;接着,重点分类介绍了节点影响力的度量方法,通过网络拓扑、用户行为和内容分析这3类方法总结了影响力的建模和度量方法;然后总结了影响力的传播和最大化模型相关成果;最后介绍了影响力的评价指标和应用.根据对现有方法的系统总结,对社会网络影响力的未来研究提出了一些值得关注的方向.
关键词:  社会网络  节点影响力  关键节点  影响力最大化  信息传播
DOI:10.13328/j.cnki.jos.005115
分类号:
基金项目:国家自然科学基金(61170112);教育部人文社会科学研究基金(13YJC860006)
Research on Node Influence Analysis in Social Networks
HAN Zhong-Ming1,2, CHEN Yan1, LIU Wen1, YUAN Bi-Hong1, LI Meng-Qi1, DUAN Da-Gao1
1.School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China;2.Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing 100048, China
Abstract:
Research on the influence of social network nodes is one of the key issues in social network analysis. Over the past decade, with the rapid development of online social networks, researchers have the opportunity to analyze and model node influence on many real social networks to achieve fruitful research results which can be applied in a wide range of applications. This paper analyzes and summarizes the main research efforts of social network influence analysis in recent years. First, different definitions of influence, influence functional scope and forms of influence are introduced. Next, models and methods of measuring of node influence are discussed and analyzed in detail with respect to network topology, user behavior and content analysis. Then, literatures about influence spreading and influence maximization model are summarized. Moreover, different indexes for evaluating influence methods are compared, and applications related influence are also presented. Finally, some future research directions on influence analysis are suggested based on the review and analysis of existing research efforts.
Key words:  social network  node influence  key node  influence maximization  information propagation

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