《计算机应用研究》|Application Research of Computers

复杂网络中的社团发现算法综述

Survey of community detection algorithms in complex network

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作者 李辉,陈福才,张建朋,吴铮,李邵梅,黄瑞阳
机构 信息工程大学,郑州 450002
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文章编号 1001-3695(2021)06-002-1611-08
DOI 10.19734/j.issn.1001-3695.2020.06.0211
摘要 社团发现方法能够用来挖掘网络中隐藏的聚簇结构信息,对复杂网络结构与功能的分析具有重要意义。近些年来,随着网络数据的爆炸式增长,网络演化的多样性,涌现出了大量能够处理不同场景的社团发现方法和框架。为了深入了解社团发现领域的研究现状和发展趋势,对复杂网络中的社团发现算法进行综述。首先,对这些算法进行了分类;其次,详细介绍了每一类算法,并进行了分析和对比;之后,介绍了一些常用的评价指标,并阐述了社团发现的应用场景;最后,对该领域未来研究方向进行了展望。
关键词 社团发现; 动态网络; 重叠社团; 深度学习
基金项目 国家自然科学基金群体项目
国家重点研发计划项目
郑州市协同创新重大专项资助项目
本文URL http://www.arocmag.com/article/01-2021-06-002.html
英文标题 Survey of community detection algorithms in complex network
作者英文名 Li Hui, Chen Fucai, Zhang Jianpeng, Wu Zheng, Li Shaomei, Huang Ruiyang
机构英文名 Information Engineering University,Zhengzhou 450002,China
英文摘要 The community detection method can mine hidden cluster structure in the network, which is of great significance for the analysis of the structure and function of complex network. In recent years, with the explosive growth of network data and the diversity of network evolution, a large number of methods and frameworks for community detection that can handle different scenarios have emerged. In order to deeply understand the research status and development trend, this paper reviewed the community detection methods. This paper firstly classified these algorithms, introduced each kind of algorithm in detail and made analysis and comparison, and then introduced some commonly used evaluation indicators and expounded the application scenarios. Finally, this paper looked forward to the future research directions of this field.
英文关键词 community detection; dynamic network; overlapping community; deep learning
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收稿日期 2020/6/22
修回日期 2020/8/18
页码 1611-1618
中图分类号 TP391
文献标志码 A