社交网络用户影响力的建模方法  

Modeling Methods of Social Network User Influence

作　　者：谭琪 张凤荔[1] 张志扬 陈学勤[1] TAN Qi;ZHANG Feng-li;ZHANG Zhi-yang;CHEN Xue-qin(School of Information and Software Engineering(Software Engineering),University of Electronic Science and Technology of China,Chengdu 610054,China)

机构地区：[1]电子科技大学信息与软件工程学院(软件工程),成都610054

出　　处：《计算机科学》2021年第2期76-86,共11页Computer Science

基　　金：国家自然科学基金(61802033,61472064,61602096);四川省科技计划(2018GZ0087,2019YJ0543);四川省区域创新合作项目(2020YFQ0018);博士后基金项目(2018M643453);广东省国家重点实验室项目(2017B030314131);网络与数据安全四川省重点实验室开放课题(NDSMS201606)。

摘　　要：社交网络用户影响力在舆情演化、广告营销及政治选举等领域有着广泛应用,研究者在过去的工作中,通过分析和建模,在影响力方面取得了一定的成果,但还存在着定义不明晰、技术落后和应用缺乏等问题。文中明确提出了社交网络用户影响力的研究模型,将传统技术与先进技术结合,并据此梳理了该领域的相关文献,主要从用户、内容特征和深度学习技术的角度论述了基于社交网络的用户影响力的研究方法,并进一步划分成本质和邻域属性、情感分析和元数据、面向局部网络和基于用户及内容特征,还介绍了节点识别的方法,为该领域的学者提供有效且全面的参考。其次,文中还介绍了用户影响力建模方法在预测应用方面的数据集、评价指标和实验结果等,旨在预测下一个激活节点。最后对其未来的发展趋势作出展望。Social network user influence has been widely used in the fields of public opinion evolution,advertising marketing,political election,etc.In the past work,researchers have achieved certain results by analyzing and modeling influence,but there are still some problems such as unclear definition,backward technology and lacking of application.This paper explicitly puts forward the research model of social network user influence,combines traditional technology and advanced technology,analyzes the related literature in this field,and mainly discusses the research methods of users influence based on the social network from the perspective of users,content features and deep learning technology,and then further divides it into nature and the neighborhood attri-butes,sentiment analysis and metadata,local network oriented and user and content based characteristics.It also introduces the methods of identification,providing effective and comprehensive reference for scholars in the field.Secondly,it also introduces the data set,evaluation index and experimental results of the prediction application of user influence modeling methods,aiming to predict the next activation node.Finally,its future development trend is prospected.

关 键 词：社交网络 用户影响力 信息扩散 级联预测 节点识别 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]