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基于社团密合度的复杂网络社团发现算法
引用本文:陈东明,王云开,黄新宇,王冬琦. 基于社团密合度的复杂网络社团发现算法[J]. 东北大学学报(自然科学版), 2019, 40(2): 186-191. DOI: 10.12068/j.issn.1005-3026.2019.02.007
作者姓名:陈东明  王云开  黄新宇  王冬琦
作者单位:东北大学 软件学院,辽宁 沈阳,110169;东北大学 软件学院,辽宁 沈阳,110169;东北大学 软件学院,辽宁 沈阳,110169;东北大学 软件学院,辽宁 沈阳,110169
基金项目:辽宁省自然科学基金资助项目(20170540320); 辽宁省博士启动基金资助项目(20170520358); 辽宁省教育厅科学研究项目(L20150167).
摘    要:传统的社团发现算法大多存在划分效果和复杂度相矛盾的问题,为了解决该问题,提出一种新的单社团结构评价标准——社团密合度(group density).在此基础上,设计了一种基于凝聚思想的社团发现算法,该算法通过不断融合小社团,使网络的社团结构向平均社团密合度最大的方向发展,并使用模块度检测算法的划分结果.通过与经典的GN,Fast Newman,LPA等算法对多个数据集进行实验对比,验证了本文算法在获得较好的划分效果的同时具有较低的时间复杂度.

关 键 词:复杂网络  社团结构  社团发现  模块度  社团密合度
收稿时间:2017-11-01
修稿时间:2017-11-01

Community Detection Algorithm for Complex Networks Based on Group Density
CHEN Dong-ming,WANG Yun-kai,HUANG Xin-yu,WANG Dong-qi. Community Detection Algorithm for Complex Networks Based on Group Density[J]. Journal of Northeastern University(Natural Science), 2019, 40(2): 186-191. DOI: 10.12068/j.issn.1005-3026.2019.02.007
Authors:CHEN Dong-ming  WANG Yun-kai  HUANG Xin-yu  WANG Dong-qi
Affiliation:School of Software, Northeastern University, Shenyang 110169, China.
Abstract:Most of the traditional community detection algorithms cannot balance partitioning effect and complexity well. So, this paper presents a new evaluation standard of single community called group density. Based on the group density, a community detection algorithm based on agglomeration is proposed. The algorithm continues to integrate small communities, and makes the community structure of the network develop in the direction of maximizing average group density. Modularity is employed to detect the partitioning effect of the algorithm. Experimental results demonstrate that the new algorithm outperforms the traditional GN, Fast Newman, LPA algorithms in multiple data sets, which shows that the algorithm proposed has better partitioning effect and lower time complexity.
Keywords:complex network  community structure  community detection  modularity  group density  
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