计算机科学 ›› 2018, Vol. 45 ›› Issue (6A): 283-289.

• 网络与通信 • 上一篇    下一篇

大规模网络总体通讯性及其效率评价分析

闫佳琪,陈俊华冷晶   

  1. 中央财经大学管理科学与工程学院 北京100081
  • 出版日期:2018-06-20 发布日期:2018-08-03
  • 作者简介:闫佳琪 女,硕士生,主要研究方向为复杂网络、数据挖掘;陈俊华 男,博士,副教授,主要研究方向为房地产金融与投资、模式识别,E-mail:junhuachen@cufe.edu.cn;冷 晶 女,硕士生,主要研究方向为数据挖掘。
  • 基金资助:
    国家自然科学基金面上项目(71473283)资助

Total Communication and Efficiency Analysis of Large Scale Networks

YAN Jia-qi,CHEN Jun-hua,LENG Jing   

  1. School of Management Science and Engineering,Central University of Finance and Economics,Beijing 100081,China
  • Online:2018-06-20 Published:2018-08-03

摘要: 复杂网络中心性测度一直是复杂网络研究的热点,本研究重点关注利用网络邻接矩阵的函数行的和来研究网络总体通讯性的概念。研究的重点包括矩阵指数和解析度,它们在图的路径方面具有天然的解释,研究表明,即使在大型网络中,所提方法也可以非常快速地计算它们。此外,提出节点的通信总和作为网络连接的有效测度,能够测算每个节点与网络的其他节点的通信程度。利用虚拟网络数据和真实数据将总体通讯性中心性度量与相关方法进行比较,结果表明总体通讯性能够有效地作为连通性的整体指标来衡量网络上的信息流动性,具有广泛的应用前景。

关键词: 复杂网络, 通信性, 网络分析, 中心性

Abstract: The centrality measure of nodes has always been a hot topic in complex network research.This paper focused on researching the concept of total communication through the sum of the functions of the network adjacency matrix.The main research includes matrix exponent and resolution,which have natural explanations on the path of the basic graph.The research proved that they can be calculated very quickly even in the case of large networks.In addition,this paper proposed the sum of the node communication as a valid measure of the network connection,which can measure the degree of communication between each node and other nodes in the network.A comparison has been made between the centrality measure of nodes and the related methods by using virtual network data and real data.The results show that the total communication capability can be used as a measure of connectivity for the overall measure of information flow on a given network,which has broad application prospects.

Key words: Centrality, Communication, Complex network, Network analysis

中图分类号: 

  • TP393
[1]CALDARELLI G.Scale-Free Networks[M].UK:Oxford University Press,2007.<br /> [2]CROFOOT M C,RUBENSTEIN D I,MAIYA A S,et al.Aggression,grooming and group-level cooperation in white-faced capuchins (Cebus capucinus):Insights from social networks[J].Amer.J.Primatol,2011,73(8):821.<br /> [3]ESTRADA E.The Structure of Complex Networks[M].UK: Oxford University Press,2011.<br /> [4]ESTRADA E,FOX M,HIGHAM D,et al.Network Science.Complexity in Nature and Technology[M].New York:Sprin-ger,2010.<br /> [5]ESTRADA E,HATANO N,BENZI M.The physics of communicability in complex networks[J].Physics Reports,2012,514(3):89-119.<br /> [6]LANGVILLE A N,MEYER C D.Google’s PageRank and Beyond:The Science of Search Engine Rankings[M].Princeton,NJ:Princeton University Press,2006.<br /> [7]NEWMAN M E J.The structure and function of complex networks[J].SIAM Review,2003,45(2):167-256.<br /> [8]SAVAS B,DHILLON I.Clustered low rank approximation of graphs in information science appli-cations[C]∥Proceedings of the 2011 SIAM Conference on Data Mining.2011:164-175.<br /> [9]BOCCALETTI S,LATORA V,MORENO Y,et al.Complex networks:Structure and dynamics[J].Physics Reports,2006,424(4/5):175-308.<br /> [10]BONACICH P.Power and centrality:a family of measures[J].America Journal of Sociology,1987,92:1170-1182.<br /> [11]BRANDES U,ERLEBACH T.Network Analysis:Methodological Foundations,Lecture Notes in Computer Science[M].New York:Springer,2005.<br /> [12]LANGVILLE A N,MEYER C D.A survey of eigenvector methods for Web information retrieval[J].SIAM Review,2005,47(1):135-161.<br /> [13]NEWMAN M E J.Networks:An Introduction[M].UK:Cam- bridge University Press,2010:174-175.<br /> [14]NEWMAN M E J,BARABSI A L,WATTS D J.The Structure and Dynamics of Networks[M].Princeton,NJ:Princeton University Press,2003.<br /> [15]BENZI M,ESTRADA E,KLYMKO C.Ranking hubs and authorities using matrix functions[J].Linear Algebra and its Applications,2013,438(5):2447-2474.<br /> [16]KATZ L.A new status index derived from socio-metric data analysis[J].Psychometrika,1953,18(11):39-43.<br /> [17]KLEINBERG J.Authoritative sources in a hyper-linked envi- ronment[J].Journal of ACM,1999,46(5):604-632.<br /> [18]LANGVILLE A N,MEYER C D.Who’s No.1? The Science of Rating and Ranking[M].Princeton,NJ:Princeton University Press,2012.<br /> [19]LEMPEL R,MORAN S.The stochastic approach for link-struc- ture analysis (SALSA) and the TKC effect[C]∥Proceedings of the Ninth International Conference on the World Wide Web.2000:387-401.<br /> [20]ESTRADA E,RODR GUEZ-VELZQUEZ J A.Subgraph centrality in complex networks[J].Physical Review E,2005(55):56-103.<br /> [21]ESTRADA E,HIGHAM D J.Network properties revealed through matrix functions[J].SIAM Review,2010,52(4):671-696.<br /> [22]BENZI M,BOITO P.Quadrature rule-based bounds for func- tions of adjacency matrices[J].Linear Algebra and its Applications,2010,433(3):637-652.<br /> [23]HIGHAM N J.Functions of Matrices:Theory and Computation[M].Philadelphia,PA,USA:Society for Industrial and Applied Mathematics,2008.<br /> [24]ESTRADA E,HATANO N.Communicability in complex networks[J].Physical Review E,2008,77(3):036111.<br /> [25]BONACICH P,LLOYD P.Eigenvector-like measures of centra- lity for asymmetric relations[J].Social Networks,2001,23(3):191-201.<br /> [26]BORGATTI S P,EVERETT M G.A graph-theoretic perspective on centrality[J].Social Networks,2006,28(4):466-484.<br /> [27]GRINDROD P,HIGHAM D.A matrix iteration for dynamic network summaries[J].SIAM Review,2013,55(1):118-128.<br /> [28]BARABSI A L,ALBERT R.Emergence of scaling in random networks[J].Science,1999,286(5439):509-512.
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