首页 | 本学科首页   官方微博 | 高级检索  
     

社会网络节点影响力分析研究
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[J].软件学报,2017,28(1):84-104.
作者姓名:韩忠明  陈炎  刘雯  原碧鸿  李梦琪  段大高
作者单位:北京工商大学计算机与信息工程学院 北京 100048;食品安全大数据技术北京市重点实验室 北京 100048,北京工商大学计算机与信息工程学院 北京 100048,北京工商大学计算机与信息工程学院 北京 100048,北京工商大学计算机与信息工程学院 北京 100048,北京工商大学计算机与信息工程学院 北京 100048,北京工商大学计算机与信息工程学院 北京 100048
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项目(13YJC860006)
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.过去的十多年间,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.本文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;接着重点分类介绍节点影响力的度量方法,从网络拓扑、用户行为和内容分析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
收稿时间:1/4/2016 12:00:00 AM
修稿时间:2016/4/25 0:00:00

Research on Node Influence Analysis in Social Networks
HAN Zhong-Ming,CHEN Yan,LIU Wen,YUAN Bi-Hong,LI Meng-Qi and DUAN Da-Gao.Research on Node Influence Analysis in Social Networks[J].Journal of Software,2017,28(1):84-104.
Authors:HAN Zhong-Ming  CHEN Yan  LIU Wen  YUAN Bi-Hong  LI Meng-Qi and DUAN Da-Gao
Affiliation:School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China;Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing 100048, China,School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China,School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China,School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China,School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China and School of Computer and Information Engineering, Beijing Technology and Business University, 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, and have achieved fruitful research results, which are applied in a wide range of applications. This paper analyzes and summarizes the main research efforts of social network influence analysis in recent years. Firstly, different definitions of influence, influence functional scope and forms of influence are introduced. Secondly, models and methods of measuring of node influence are discussed and analyzed detailed, which are classified into three classes:network topology, user behavior and content analysis. After that, we summarize main papers about influence spreading and influence maximization model. Moreover, we compare different indexes for evaluating influence methods, and applications related influence are also introduced. Finally, we conclude the paper with some future research directions on influence analysis based on the summary and analysis of existing research efforts.
Keywords:Social network  Node influence  Key node  Information propagation
点击此处可从《软件学报》浏览原始摘要信息
点击此处可从《软件学报》下载全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号