在线社交网络的社区发现研究进展

doi:10.13266/j.issn.0252-3116.2020.09.016

图书情报工作 ›› 2020, Vol. 64 ›› Issue (9): 142-152.DOI: 10.13266/j.issn.0252-3116.2020.09.016

在线社交网络的社区发现研究进展

张海涛^1,2, 周红磊¹, 张鑫蕊¹, 孙彤¹

1 吉林大学管理学院长春 130022;
2 吉林大学信息资源研究中心长春 130022

收稿日期:2019-11-12 修回日期:2020-02-07 出版日期:2020-05-05 发布日期:2020-05-05
作者简介:张海涛(ORCID:0000-0002-9421-8187),教授,博士生导师,E-mail:zhtinfo@126.com;周红磊(ORCID:0000-0002-9732-8138),硕士研究生;张鑫蕊(ORCID:0000-0001-9413-6109),硕士研究生;孙彤(ORCID:0000-0002-0068-0275),硕士研究生。

Research Progress in Community Detection of Online Social Networks

Zhang Haitao^1,2, Zhou Honglei¹, Zhang Xinrui¹, Sun Tong¹

1 Management School of Jilin University, Changchun 130022;
2 The Information Resource Research Center of Jilin University, Changchun 130022

Received:2019-11-12 Revised:2020-02-07 Online:2020-05-05 Published:2020-05-05

摘要/Abstract

摘要： [目的/意义] 以在线社交网络为研究对象，通过文献梳理准确捕捉社区发现的发展态势及研究热点，探究如何在大规模社交网络中挖掘隐藏社区，具有理论和实践意义。[方法/过程] 以中国知网（CNKI）数据库、Web of Science核心合集及相关国际会议文献作为数据来源，应用CiteSpace可视化分析工具从热点关键词、主题演化路径以及共被引文献等方面进行定量研究，并从社区发现方法、算法实现及应用实践3个维度对文献内容详细述评。[结果/结论] 当前研究领域仍有广阔的发展空间，未来应注重算法的优化及创新、应用场景的区分和拓展以及融合跨学科知识、前沿技术方法的交叉研究。

关键词: 在线社交网络, 社区发现, 动态社区演化, 研究进展

Abstract: [Purpose/significance] Taking online social network as the research object, and through the literature combing to accurately capture the development trend and research hotspots of community discovery, and exploring how to mine hidden communities in large-scale social networks, which has theoretical and practical significance. [Method/process] Using CNKI database, Web of Science core collection and related international conference documents as data sources. The CiteSpace visual analysis tool was used to quantitatively study hotspot keywords, topic evolution paths and co-cited documents. And the topic research content was reviewed from 3 dimensions:community discovery method, algorithm implementation and application practice. [Result/conclusion] There is still much room for development in the current research field. In the future, we should pay attention to optimization and innovation of algorithms, differentiation and expansion of application scenarios, and cross-disciplinary research on interdisciplinary knowledge and cutting-edge technology methods.

Key words: online social network, community detection, dynamic community evolution, research progress

中图分类号:

G250

张海涛, 周红磊, 张鑫蕊, 孙彤. 在线社交网络的社区发现研究进展[J]. 图书情报工作, 2020, 64(9): 142-152.

Zhang Haitao, Zhou Honglei, Zhang Xinrui, Sun Tong. Research Progress in Community Detection of Online Social Networks[J]. LIS, 2020, 64(9): 142-152.

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在线社交网络的社区发现研究进展

Research Progress in Community Detection of Online Social Networks

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