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复杂系统与复杂性科学  2017, Vol. 14 Issue (3): 8-29    DOI: 10.13306/j.1672-3813.2017.03.002
  本期目录 | 过刊浏览 | 高级检索 |
复杂网络模糊重叠社区检测研究进展
肖婧1, 张永建1, 许小可1,2
1.大连民族大学信息与通信工程学院,辽宁 大连 116600;
2.贵州省公共大数据重点实验室贵州大学,贵阳 550025
Research Progress of Fuzzy Overlapping Community Detection in Complex Networks
XIAO Jing1, ZHANG Yongjian1, XU Xiaoke1,2
1.College of Information and Communication Engineering, Dalian Minzu University, Dalian 116600, China;
2.Guizhou Provincial Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China
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摘要 模糊重叠社区检测通过扩展隶属度取值空间,实现了重叠节点与社区之间复杂且模糊隶属关系的精确化测量,不仅能够有效提升重叠社区结构检测的精确性,而且能够深度挖掘出节点和社区的重叠特性。文中首先分析了模糊重叠社区检测与传统离散重叠社区检测的关系;然后对二者的国内外相关研究现状进行阐述和分析,其中在模糊重叠社区检测方法研究中根据模糊隶属度获取方式的不同将当前相关研究分为扩展标签传播、非负矩阵分解、基于边界节点的两阶段检测、模糊聚类、模糊模块度优化五大类进行综述,重点分析了基于进化算法的模糊模块度优化方法;最后对模糊重叠社区检测研究未来的发展趋势进行了分析和展望。
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肖婧
张永建
许小可
关键词 社区检测离散重叠模糊重叠模糊模块度优化进化算法    
Abstract:Through expanding value space, fuzzy overlapping detection redefines the fuzzy membership degree, which can not only improve the detection accuracy of the complicated community structures, but also explore the overlapping features of nodes and communities. In this paper, we firstly give the explanation of the difference between crisp and fuzzy overlapping detection, and then summarize their related researches. To clearly state the fuzzy overlapping detection, we introduce the available work by dividing them into five classes on the acquisition method of fuzzy membership degree, including expanded label propagation, nonnegative matrix factorization, edge nodes based two-phase detection, fuzzy clustering and fuzzy modularity optimization. The advances and challenges of the fuzzy modularity optimization based on evolutionary algorithms are discussed in detail. At last some future research topics are given
Key wordscommunity detection    crisp overlap    fuzzy overlap    fuzzy modularity optimization    evolutionary algorithm
收稿日期: 2016-12-12      出版日期: 2019-01-10
ZTFLH:  TP399  
基金资助:国家自然科学基金(61374170,61603073,61773091)、辽宁省自然科学基金(201602200)、辽宁省博士科研启动基金(201601294)、中央高校基本科研业务费专项基金(DC201502060201,DCPY2016002)、黑龙江省博士后科学基金(LBH-Z12073)
通讯作者: 许小可(1979),男,辽宁庄河人,博士,教授,主要研究方向为网络科学和社交网络大数据。   
作者简介: 肖婧(1985),女,湖北十堰人,博士,讲师,主要研究方向为高维多目标优化和网络社团检测。
引用本文:   
肖婧, 张永建, 许小可. 复杂网络模糊重叠社区检测研究进展[J]. 复杂系统与复杂性科学, 2017, 14(3): 8-29.
XIAO Jing, ZHANG Yongjian, XU Xiaoke. Research Progress of Fuzzy Overlapping Community Detection in Complex Networks. Complex Systems and Complexity Science, 2017, 14(3): 8-29.
链接本文:  
http://fzkx.qdu.edu.cn/CN/10.13306/j.1672-3813.2017.03.002      或      http://fzkx.qdu.edu.cn/CN/Y2017/V14/I3/8
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