复杂网络社区发现研究进展

doi:10.11896/jsjkx.190100214

计算机科学 ›› 2020, Vol. 47 ›› Issue (2): 10-20.doi: 10.11896/jsjkx.190100214

所属专题：网络通信

• 数据库&大数据&数据科学 • 上一篇下一篇

复杂网络社区发现研究进展

赵卫绩^1,2,张凤斌¹,刘井莲^2,3

(哈尔滨理工大学计算机科学与技术学院哈尔滨150080)¹;
(绥化学院信息工程学院黑龙江绥化152061)²;
(东北大学计算机科学与工程学院沈阳110819)³

收稿日期:2019-01-25 出版日期:2020-02-15 发布日期:2020-03-18
通讯作者: 张凤斌(zhangfb@hrbust.edu.cn)
基金资助:
国家自然科学基金(61172168,61772122);黑龙江省省属高校基本科研业务费科研项目(KYYWF10236180104,KYYWF10236180207)

Review on Community Detection in Complex Networks

ZHAO Wei-ji^1,2,ZHANG Feng-bin¹,LIU Jing-lian^2,3

(School of Computer Science and Technology,Harbin University of Science and Technology,Harbin 150080,China)¹;
(School of Information Engineering,Suihua University,Suihua,Heilongjiang 152061,China)²;
(School of Computer Science and Engineering,Northeastern University,Shenyang 110819,China)³

Received:2019-01-25 Online:2020-02-15 Published:2020-03-18
About author:ZHAO Wei-ji,born in 1980,doctorial student,associate professor,is member of China Computer Federation (CCF).His main research interests include community detection and data mining;ZHANG Feng-bin,born in 1965,Ph.D,professor,Ph.D supervisor,is member of China Computer Federation (CCF).His main research interests include network security and intrusion detection technology.
Supported by:
This work was supported by the National Natural Science Foundation of China (61172168, 61772122) and Fundamental Research Funds for the Universities of Heilongjiang Provincial Department of Education of China (KYYWF10236180104, KYYWF10236180207).

摘要/Abstract

摘要： 近年来,随着现代网络通信和社会媒体等技术的飞速发展,复杂网络成为多学科交叉研究的热点之一,社区发现是复杂网络中的一个重要问题,对其进行研究具有重要的理论意义和应用价值。该问题吸引了多个学科领域的众多学者的关注,并且已有许多社区发现算法被提出。已有的社区发现综述多是侧重某一方向或特定领域展开,基于此,文中在之前工作的基础上,对国内外社区发现工作进行了深入调研,较全面地阐述了复杂网络社区发现的研究现状。首先,针对不同网络结构,给出社区发现的问题定义和主要的评价指标。然后,介绍了不同网络结构中的经典社区发现算法,包括同质网络中的全局社区发现、局部社区发现算法,异质网络中的二分网络、三分网络和多分网络中的社区发现,结合节点内容和连接结构的社区发现算法,以及动态网络中的社区发现和社区演化工作。最后,简要介绍了社区发现的典型应用,包括影响最大化、链路预测和情感分析领域的应用。此外,探讨了当前社区发现研究面临的主要挑战,试图为社区发现研究领域勾画一个较为清晰和全面的轮廓,为初学者提供指引。

关键词: 动态网络, 社区发现, 社区结构, 同质网络, 异质网络

Abstract: In recent years,with the rapid development of modern network communication and social media technologies,complex network has become one of the frontier hotspots of interdisciplinary research.As an important problem in the research of complex network,community detection has important theoretical significance and application value,and has attracted increasing attention.Many community detection algorithms and reviews have been proposed.However,most of the existing reviews on community detection focus on a particular direction or field.On the basis of previous work,this paper did deep research in the community detection algorithms,and gave a review on the research progress of community detection.Firstly,this paper gave the definition of community detection and evaluation measurements for different network structure.Then,this paper introduced the classic community detection algorithms on different network structure,including the global community detection and local community detection algorithms on homogeneous networks,community detection on heterogeneous network,and community detection on link structure combined with node content,as well as the dynamic network community detection and community evolution.Finally,this paper briefly introduced the typical applications of community discovery in the real world,including impact maximization,link prediction and emotion analysis application.In addition,this paper discussed the challenges in the current community discovery field.This paper try to draw a clearer and more comprehensive outline for the community detection research field,and provide a good guide for beginners in the community detection.

Key words: Community detection, Community structure, Dynamic network, Heterogeneous network, Homogeneous network

中图分类号:

TP391

赵卫绩,张凤斌,刘井莲. 复杂网络社区发现研究进展[J]. 计算机科学, 2020, 47(2): 10-20. https://doi.org/10.11896/jsjkx.190100214

ZHAO Wei-ji,ZHANG Feng-bin,LIU Jing-lian. Review on Community Detection in Complex Networks[J]. Computer Science, 2020, 47(2): 10-20. https://doi.org/10.11896/jsjkx.190100214

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[15]	富坤, 仇倩, 赵晓梦, 高金辉. 基于节点演化分阶段优化的事件检测方法 Event Detection Method Based on Node Evolution Staged Optimization 计算机科学, 2020, 47(5): 96-102. https://doi.org/10.11896/jsjkx.190400072