计算机科学 ›› 2017, Vol. 44 ›› Issue (Z6): 419-423.doi: 10.11896/j.issn.1002-137X.2017.6A.094

• 大数据与数据挖掘 • 上一篇    下一篇

一种基于节点特征向量的复杂网络社团发现算法

陆亿红,张振宁,杨雄   

  1. 浙江工业大学计算机学院 杭州310023,浙江工业大学计算机学院 杭州310023,浙江工业大学计算机学院 杭州310023;常州工学院计算机信息工程学院通信工程系 常州213002
  • 出版日期:2017-12-01 发布日期:2018-12-01
  • 基金资助:
    本文受水利部公益性行业科研专项(201401044),常州市科技计划项目(CJ20159013)资助

Community Structure Detection Algorithm Based on Nodes’ Eigenvectors

LU Yi-hong, ZHANG Zhen-ning and YANG Xiong   

  • Online:2017-12-01 Published:2018-12-01

摘要: 社团结构是复杂网络的一种很普遍且非常重要的拓扑特征,社团的发现有助于了解复杂网络的结构和功能。节点间相似度的评价指标对于社团发现的结果起着至关重要的作用,传统算法中使用的相似度指标存在着时间复杂度过高和不够精确的缺陷。为了弥补这两个缺陷,在信息传递理论的基础上将网络中的节点抽象成了多维数据集,结合传统聚类算法K-means提出了一种社团发现的新算法。基于Zachary Karate Club网络、Jazz Musician网络和Facebook网络的实验结果表明,该算法是高效且准确的。

关键词: 复杂网络,社团结构,信息传递理论,特征向量

Abstract: Community structure is one of the ubiquitous and significant topology characteristics of complex network.It can help us to learn the structure and functions of a complex network.The similarity index plays a vital role in community detection but it has the shortage of high time complexity and low accuracy.In order to improve the two shortages,nodes are abstracted from a complex network into a multi-dimension data set based on the theory of information transmission in the network.Combined with the traditional clustering algorithm K-means,a new community detection algorithm was proposed.The experimental results obtained from Zachary Karate Club network,Jazz Musician network and Facebook network show that the algorithm is effective and accurate.

Key words: Complex networks,Community structure,Theory of information transmission,Eigenvector

[1] WATTS D J.A twenty-first century science[J].Nature,2007,445(7127):489-489.
[2] REN G,WANG X.Epidemic spreading in time-varying community networks[J].Chaos:An Interdisciplinary Journal of Nonlinear Science,2014,24(2):023116.
[3] GIRVAN M,NEWMAN M E J.Community structure in social and biological networks[J].Proceedings of the national academy of sciences,2002,99(12):7821-7826.
[4] MEHRA A.The Development of Social Network Analysis:A Study in the Sociology of Science[J].Social Networks,2005,27(4):377-384.
[5] NEWMAN M E,GIRVAN M.Finding and evaluating community structure in networks[J].Physical Review E Statistical Nonlinear & Soft Matter Physics,2004,69(2 Pt 2):026113-026113.
[6] NEWMAN M E J.Detecting community structure in networks[J].The European Physical Journal B-Condensed Matter and Complex Systems,2004,38(2):321-330.
[7] NEWMAN M E J.Fast algorithm for detecting communitystructure in networks[J].Physical Review E,2004,69(6):066133.
[8] NEWMAN M E J.Clustering and preferential attachment ingrowing networks[J].Physical Review E,2001,64(2):025102.
[9] ZHOU T,Lü L,ZHANG Y C.Predicting missing links via local information[J].The European Physical Journal B-Condensed Matter and Complex Systems,2009,71(4):623-630.
[10] FORTUNATO S,LATORA V,MARCHIORI M.Method tofind community structures based on information centrality[J].Physical review E,2004,70(5):056104.
[11] 刘海峰,刘守生,张学仁.聚类模式下一种优化的 K-means 文本特征选择[J].计算机科学,2011,38(1):195-197.
[12] ZHANG T,WU B.A method for local community detection by finding core nodes[C]∥Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mi-ning (ASONAM 2012).IEEE Computer Society,2012:1171-1176.
[13] 邓智龙,淦文燕.复杂网络中的社团结构发现方法[J].计算机科学,2012,39(z6):103-108.
[14] GLEISER P M,DANON L.Community structure in jazz[J].Advances in complex systems,2003,6(4):565-573.
[15] ZACHARY W W.An information flow model for conflict and fission in small groups[J].Journal of Anthropological Research,1977,33(4):452-473.
[16] 张聪,沈惠璋,李峰,等.复杂网络中社团结构发现的多分辨率密度模块度[J].物理学报,2012,61(14):148902-148902.

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