Nothing Special   »   [go: up one dir, main page]

计算机科学 ›› 2015, Vol. 42 ›› Issue (Z11): 296-300.

• 网络与通信 • 上一篇    下一篇

利用KSN算法发现网络中有影响力的结点

田艳,刘祖根   

  1. 云南财经大学信息学院 昆明650221,云南财经大学信息学院 昆明650221
  • 出版日期:2018-11-14 发布日期:2018-11-14
  • 基金资助:
    本文受云南省社科规划项目(YB2012080),云南省自然科学基金项目(2011FZ148),云南财经大学研究生创新基金项目(云财研创(2014)24),云南财经大学校级项目(YC2012D11),云南财经大学研究生创新基金项目(2015YUFEYC004)资助

Detecting Most Influential Nodes in Complex Networks by KSN Algorithm

TIAN Yan and LIU Zu-gen   

  • Online:2018-11-14 Published:2018-11-14

摘要: 准确高效地发现网络中有影响力的传播者具有非常重要的理论和现实意义。近年来,结点影响力排序受到了多领域学者的广泛关注。K-shell是一种较好的结点影响力评价指标;然而,仅仅依赖结点自身K-shell值实现的算法通常具有评估结果精确度不高、适用性较差等缺陷。针对此问题,提出KSN(the K-shell and neighborhood centrality)中心性模型,该算法综合考虑了结点本身及其所有二阶以内邻居结点的K-shell值。实验结果表明,所提出算法 度量结点传播的能力 比度中心性、介数中心性、K-shell分解、混合度分解等方法更准确。

关键词: 复杂网络,有影响力结点,中心化测量,KSN中心性

Abstract: It is very important in theory and practice to detect influential spreaders in networks accurately and efficiently.Recently,scholars from various fields have paid their attention to the study of ranking nodes.The K-shell index is a relatively powerful indicator to estimate the spreading ability of nodes.However,due to only attributes of the node itself being considered,limitation in accuracy and universal applicability will exist for K-shell decomposition.To solve this problem,this paper proposed a novel algorithm called KSN (the K-shell and neighborhood centrality) to estimate the spreading influence of a node by its K-shell value and the K-shell indexes of its nearest and next nearest neighbors.Experimental results demonstrate that this proposed algorithm acts more precisely in detecting the most influential nodes than degree centrality,betweenness centrality,K-shell decomposition and the mixed degree decomposition,et al.

Key words: Complex networks,Influential nodes,Centrality measure,KSN centrality

[1] 马杰良,宋艳,潘贞贞,等.图书漂流网络模型实证研究[J].计算机科学,2015,42(3):51-54
[2] 周涛,傅忠谦,牛永伟,等.复杂网络上传播动力学研究综述[J].自然科学进展,2005,5(5):513-523
[3] 李翔,刘宗华,汪秉宏.网络传播动力学[J].复杂系统与复杂性科学,2010,Z1:41-45
[4] Liu J G,Wang Z T,Dang Y Z.Optimization of scale-free network for random failures[J].Mod.Phys.Lett.B,2006,20:815-820
[5] Brummitt C D,D’Souza R M,Leicht E.Suppressing cascades of load in interdependent networks[J].Proc Natl Acad Sci USA,2012,109:E680-E689
[6] Burt R S,Minor M J,Alba R D.Applied network analysis:A methodological introduction[M].Sage Publications Beverly Hills,1983
[7] Sabidussi G.The centrality index of a graph[J].Psychometrika,1966,31:581-603
[8] Freeman L C.A set of measures of centrality based on betweenness[J].Sociometry,1977, 40:35
[9] Yan G,Zhou T,Hu B,et al.Efficient routing on complex networks[J].Phys Rev E,2006,73:1-5
[10] Freeman L C.Centrality in social networks conceptual clarification[J].Soc Netw,1979,1:215-239
[11] Kitsak M,Gallos L K,Havlin S,et al.Identification of influential spreaders in complex networks[J].Nat Phys,2010,6:888-893
[12] Zeng A,Zhang C J.Ranking spreaders by decomposing complex networks[J].Phys.Lett.A,2013,377(14):1031-1035
[13] Liu J G,Ren Z M,Guo Q.Ranking the spreadinginfluencein complex networks[J].Physica A,2013,392:4154-4159
[14] Newman M E J.A measure of betweenness centrality based on random walks[J].Soc.Netw.,2005,27 (1):39-54
[15] Bonacich P.Factoring and weighting approaches to status scores and clique identification[J].J Math Sociol,1972,2:113-120
[16] Hethcote H W.The mathematics of infectious disease[J].Soc.Industr.Appl.Math,2000,42:599-653
[17] Blower S,Bernoulli D.An attempt at a new analysis of the mortality caused by smallpox and of the advantages of inoculation to prevent it[J].Rev.Med.Virol,2004,14:275-288
[18] Anderson R M,Robert M.Infectious Diseases of Humans:Dynamics and Control[M].New York:Oxford University Press,1992:66
[19] Diekmann O,Heesterbeek J A P.Mathematical Epidemiology of Infectious Diseases:Model Building,Analysis and Interpretation[M].New York:Wiley Series in Mathematical & Computational Biology,2001
[20] Kendall M.A new measure of rank correlation[J].Biometrika,1938,30:81-93
[21] Zachary W W.An information flow model for conflict and fission in small groups[J].J.Anthropol.Res.,1977,33:452-473
[22] Newman M E J.Finding community structure in networks using the eigenvectors of matrices[J].Phys.Rev.E,2006,74 (3):036104
[23] Guimera R,Danon L,Diaz-Guilera A,et al.Self-similar community structure in a network of human interactions[J].Phys.Rev.E,2003,8:065103
[24] Xie N.Social network analysis of blogs[D].UK:University of Bristol,2006

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!