Detecting community structure in networks by representative energy

被引:0
|
作者
Ji Liu
Guishi Deng
机构
[1] Dalian University of Technology,Institute of System Engineering
[2] Xinjiang University of Finance and Economics,College of Statistics and Information
关键词
network community; community detection; representative energy; spectral analysis; weighted eigenvector;
D O I
暂无
中图分类号
学科分类号
摘要
Network community has attractedmuch attention recently, but the accuracy and efficiency in finding a community structure is limited by the lower resolution of modularity. This paper presents a new method of detecting community based on representative energy. The method can divide the communities and find the representative of community simultaneously. The communities of network emerges during competing for the representative among nodes in network, thus we can sketch structure of the whole network. Without the optimizing by modularity, the community of network emerges with competing for representative among those nodes. To obtain the proximate relationships among nodes, we map the nodes into a spectral matrix. Then the top eigenvectors are weighted according to their contributions to find the representative node of a community. Experimental results show that the method is effective in detecting communities of networks.
引用
收藏
页码:366 / 372
页数:6
相关论文
共 50 条
  • [21] Representative community divisions of networks
    Alec Kirkley
    M. E. J. Newman
    Communications Physics, 5
  • [22] Detecting Overlapping Community Structure of Complex Networks in Nature and Society
    Miao, Shimin
    Wan, Wanggen
    Yu, Xiaoqing
    Thuillier, Etienne
    2014 INTERNATIONAL CONFERENCE ON AUDIO, LANGUAGE AND IMAGE PROCESSING (ICALIP), VOLS 1-2, 2014, : 584 - 587
  • [23] Detecting the community structure in complex networks based on quantum mechanics
    Niu, Yan Qing
    Hu, Bao Qing
    Zhang, Wen
    Wang, Min
    PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2008, 387 (24) : 6215 - 6224
  • [24] Adaptive Algorithms for Detecting Community Structure in Dynamic Social Networks
    Nguyen, Nam P.
    Dinh, Thang N.
    Xuan, Ying
    Thai, My T.
    2011 PROCEEDINGS IEEE INFOCOM, 2011, : 2282 - 2290
  • [25] A vector partitioning approach to detecting community structure in complex networks
    Wang, Gaoxia
    Shen, Yi
    Ming Ouyang
    COMPUTERS & MATHEMATICS WITH APPLICATIONS, 2008, 55 (12) : 2746 - 2752
  • [26] Detecting community structure in networks using edge prediction methods
    Yan, Bowen
    Gregory, Steve
    JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT, 2012,
  • [27] Detecting community structure of networks using evolutionary coordination games
    Cao, Lang
    Li, Xiang
    Han, Lin
    2013 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS), 2013, : 2533 - 2536
  • [28] Detecting community structure in complex networks by optimal rearrangement clustering
    Wang, Rui-Sheng
    Wang, Yong
    Zhang, Xiang-Sun
    Chen, Luonan
    EMERGING TECHNOLOGIES IN KNOWLEDGE DISCOVERY AND DATA MINING, 2007, 4819 : 119 - +
  • [29] Detecting community structure in complex networks via node similarity
    Pan, Ying
    Li, De-Hua
    Liu, Jian-Guo
    Liang, Jing-Zhang
    PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2010, 389 (14) : 2849 - 2857
  • [30] Detecting community structure in complex networks via resistance distance
    Zhang, Teng
    Bu, Changjiang
    PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2019, 526