The Strength of Structural Diversity in Online Social Networks
作者机构：Department of AutomationShanghai Jiao Tong University and Key Laboratory of System Control and Information ProcessingMinistry of Education of ChinaShanghai 200240China Department of Media and Communication and School of Data ScienceCity University of Hong KongHong Kong S.A.R.China Department of AutomationShanghai UniversityShanghai 200444China Media LabMassachusetts Institute of TechnologyCambridgeMA 02139USA
出 版 物：《Research》 (研究（英文）)
年 卷 期：2021年第2021卷第1期
页 面：1203-1212页
核心收录：
学科分类：08[工学] 081201[工学-计算机系统结构] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：supported by the National Natural Science Foundation of China(Grant Nos.61773255 and 61873167) Hong Kong RGC(GRF 11505119) City University of Hong Kong(CCR 9360120 and HKIDS 9360163)
主 题：collective networks founding
摘 要：Understanding the way individuals are interconnected in social networks is of prime significance to predict their collective *** a large-scale dataset from a knowledge-sharing website,this paper presents an exploratory investigation of the way to depict structural diversity in directed networks and how it can be utilized to predict one’s online social *** capture the structural diversity of an individual,we first consider the number of weakly and strongly connected components in one’s contact neighborhood and further take the coexposure network of social neighbors into *** show empirical evidence that the structural diversity of an individual is able to provide valuable insights to predict personal online social reputation,and the inclusion of a coexposure network provides an additional ingredient to achieve that *** synthetically controlling several possible confounding factors through matching experiments,structural diversity still plays a nonnegligible role in the prediction of personal online social *** work constitutes one of the first attempts to empirically study structural diversity in directed networks and has practical implications for a range of domains,such as social influence and collective intelligence studies.
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