基于社会网络的结构多样性研究综述

Review of Structural Diversity Studies on Social Networks

【目的】对基于社会网络的结构多样性相关研究成果进行总结梳理并展望,为后续的相关研究提供参考与借鉴。【文献范围】以“Structural Diversity”“Structural Diversity and Social Networks”“结构多样性”“结构多样性and社会网络”等检索式分别在Web of Science、Microsoft Academic、DBLP等英文数据库以及CNKI、万方、维普等中文数据库进行检索,限定发表时间为2012年4月至2022年4月,共得到1619篇文献,经过整理阅读并通过引文网络或数据库检索等方式对代表性文献涉及的相关理论进行溯源,最终筛选出55篇相关文献进行评述。【方法】对结构多样性进行理论溯源,分析概括其存在的问题,从模型改进、高效算法、实际应用三个主要方面论述结构多样性的研究现状,并对未来研究提出展望。【结果】结构多样性为基于网络拓扑结构特征,研究影响个体做出重大决策机制的模型。但原始模型存在普适性较差、模型精度不够高等问题,与图挖掘技术结合优化后表现优秀,已被应用于多领域中。【局限】只针对结构多样性研究进行梳理总结,未能与其他社会传染理论进行比较。【结论】图挖掘算法可以在一定程度上消除结构多样性模型存在的群体划分缺陷;结构多样性可以作为寻找高影响力节点的指标且需要高效搜索算法作为支撑;结构多样性已在行为预测、链接预测等领域有所应用,并可与其他特征组合优化模型,但依旧需要更多实际应用的检验。

[Objective]This paper reviews the latest developments of the structural diversity studies on social networks and discusses their future directions.[Coverage]We searched the Web of Science,Microsoft Academic,DBLP,CNKI,Wanfang Data and VIP with“Structural Diversity”,“Structural Diversity and Social Networks”.A total of 55 representative and related literature published from April 2012 to April 2022 were retrieved.[Methods]First,we traced to the source of structural diversity studies and analyzed their existing issues.Then,we examined the structural diversity research from three perspectives:model improvements,efficient algorithms,and practical applications.Finally,we discussed future works.[Results]Structural diversity is a model based on network topology features,which studies factors affecting individuals’major decision makings.The original model has the bad universality and low precision issues.Combined with graph mining technology,the new model performs well and has been applied in many fields.[Limitations]We only summarized research on structural diversity and did not compare them with other social contagion theories.[Conclusions]Graph mining algorithm could improve the structural diversity model in group division.Structural diversity is an indicator for finding highly influential nodes and required by efficient search algorithms.Structural diversity has been applied in the fields of behavior and link predictions.Features optimizing this model merit more research to evaluated their performance.