复杂网络社团发现综述  

Survey of Community Discovery in Complex Networks

作　　者：曹金鑫 许伟忠 金弟 丁卫平[1] CAO Jinxin;XU Weizhong;JIN Di;DING Weiping(School of Information Science and Technology,Nantong University,Nantong,Jiangsu 226019,China;College of Intelligence and Computing,Tia)

机构地区：[1]南通大学信息科学技术学院,江苏南通226019 [2]天津大学智能与计算学部,天津300350

出　　处：《计算机科学》2023年第S02期402-412,共11页Computer Science

基　　金：国家自然科学基金面上项目(61876128,61976120);江苏省自然科学基金面上项目(BK20191445);江苏省高等学校自然科学研究面上项(21KJB520018)。

摘　　要：现实世界中许多复杂系统均被建模成复杂网络,如社交网络、科学家协作网络等。复杂网络的研究吸引了不同领域的诸多研究者的广泛关注。挖掘社团结构,即将网络划分到具有类内链接稠密、类间链接稀疏的不同社团,是复杂网络研究的问题之一。研究复杂网络社团检测对分析复杂网络中潜在结构、规律以及社团的形成有着至关重要的意义,并且有着广泛的应用前景。鉴于复杂网络中同时包含了网络拓扑与节点内容,结合节点内容的社团检测研究将成为该研究领域的新趋势之一。文中介绍了复杂网络社团检测的研究背景和研究意义;并从基于网络拓扑、基于节点内容和基于网络拓扑和节点内容融合3个方面,较为全面地对社团检测研究现状进行了梳理以及对其面临的问题进行了分析。从3类社团检测方法中选择了10种具有代表性的算法,对它们进行性能对比和时间复杂度分析,以期望描绘关于社团发现新趋势的清晰轮廓。Many complex systems in the real world can be modeled as complex networks,such as social networks and scientist collaboration networks.The study of complex networks has attracted the attention of many researchers in different fields.The mining of community structure,division of a network into different communities of nodes with dense intra-community links and sparse inter-community links,is one of the main problems in the study of complex network.Research on community detection in complex networks is of vital importance to the analysis of the potential structure,laws,and the formation of communities in complex networks,and has a wide range of application prospects.Since complex networks contain both network topology and node content,the study of community detection combining node content will become one of the new trends in this field.This paper introduces the research background and significance of community detection in complex networks.And from three aspects based on network topology,node content,and network topology and node content integration,we comprehensively sort out the research status of community detection and analyze the problems it faces.We select 10 representative algorithms from the mentioned three types of community detection methods,and compare their performance of identifying communities and analyze time complexity of these algorithms,hoping to draw a clear outline of the new trend of community discovery.

关 键 词：复杂网络 社交网络 社团发现 节点内容 信息融合 

分 类 号：TP182[自动化与计算机技术—控制理论与控制工程]