基于介度熵的复杂网络节点重要度识别方法

兰州理工大学学报 ›› 2020, Vol. 46 ›› Issue (2): 111-115.

• 自动化技术与计算机技术 • 上一篇下一篇

基于介度熵的复杂网络节点重要度识别方法

卢鹏丽¹, 郭旭东¹, 董璊¹, 曹乐²

1.兰州理工大学计算机与通信学院, 甘肃兰州 730050;
2.天水师范学院电子信息与电气工程学院, 甘肃天水 741000

收稿日期:2018-10-29 出版日期:2020-04-28 发布日期:2020-06-23
作者简介:卢鹏丽(1973-),女,甘肃酒泉人,博士,教授, 博导.
基金资助:
国家自然科学基金(11361033)

Importance identification method of complex network nodes based on betweenness and degree entropy

LU Peng-li¹, GUO Xu-dong¹, DONG Men¹, CAO Le²

1. School of Computer and Communication, Lanzhou Univ. of Tech. , Lanzhou 730050, China;
2. College of Electronic Information and Electrical Engineering, Tianshui Normal University, Tianshui 741000, China

Received:2018-10-29 Online:2020-04-28 Published:2020-06-23

摘要/Abstract

摘要： 现有复杂网络通常会受到随机攻击和蓄意攻击,导致复杂网络拓扑结构的可靠性性能下降.为了解决以上问题,基于图熵,结合节点的介数中心性和其所有邻居节点的度中心性,提出一种新的攻击策略,即介度熵( BE),用来识别网络中的重要节点并加以保护.实验分别通过静态攻击和动态攻击来评估攻击策略在3个标准网络模型和3个真实网络上的效率,通过比较,介度熵比传统的攻击策略具有更高的攻击效率.

关键词: 复杂网络, 中心性, 攻击策略, 抗毁性, 介度熵

Abstract: Existent complex networks will usually be subject to random attacks and deliberate attacks, resulting in reduced reliability of complex network topologies. In order to solve the above-mentione problems, a new attack strategy is proposed based on graph entropy and incorporated with the betweenness centrality of nodes and the degree centrality of all their neighbor nodes, namely the betweenness and degree entropy (BE), which is used to identify important nodes in the network and protect them. Experimentally, the efficiency of attack strategies of three standard network models and three real networks is evaluated under condition of static attacks and dynamic attacks. By means of comparison, it is found that the betweenness and degree entropy will have higher attack efficiency than the traditional attack strategy.

Key words: complex network, centrality, attack strategy, invulnerability, betweenness and degree entropy

中图分类号:

TP393

卢鹏丽, 郭旭东, 董璊, 曹乐. 基于介度熵的复杂网络节点重要度识别方法[J]. 兰州理工大学学报, 2020, 46(2): 111-115.

LU Peng-li, GUO Xu-dong, DONG Men, CAO Le. Importance identification method of complex network nodes based on betweenness and degree entropy[J]. Journal of Lanzhou University of Technology, 2020, 46(2): 111-115.

参考文献

[1] MOTTER A E,ALBERT R.Networks in motion [J].Physics Today,2012,65(4):43-48.
[2] ALBERT R.Diameter of the world wide web [J].Nature,1999,401(6):130-131.
[3] FREEMAN L C.A set of measures of centrality based on betweenness [J].Sociometry,1977,40(1):35-41.
[4] SABIDUSSI G.The centrality index of a graph [J].Psychometrika,1966,31(4):581-603.
[5] NEWMAN M E J.Finding community structure in networks using the eigenvectors of matrices [J].Physical Review E Statistical Nonlinear & Soft Matter Physics,2006,74(3):036104.
[6] NIE T,GUO Z,ZHAO K,et al.Using mapping entropy to identify node centrality in complex networks [J].Physica A Statistical Mechanics & Its Applications,2016,453:290-297.
[7] GIALAMPOUKIDIS I,KALPAKIS G,TSIKRIKA T,et al.Key player identification in terrorism-related social media networks using centrality measures [C]//2016 European Intelligence and Security Informatics Conference (EISIC).[S.l.]:IEEE,2016.
[8] GAO X,LI K,CHEN B.Invulnerability measure of a military heterogeneous network based on network structure entropy [J].IEEE Access,2018,6:6700-6708.
[9] CHEN Jueyu,PENG Minfang,GAO Xiang,et al.Multi-objective distribution network planning considering invulnerability [C]//Technology,Networking,Electronic & Automation Control Conference.[S.l.]:IEEE,2018.
[10] 董政呈,方彦军,田猛.相互依存网络抗毁性研究综述 [J].复杂系统与复杂性科学,2017(3):30-44.
[11] 付秀琴,李腾.不同攻击策略下快递配送网络抗毁性分析 [J].经济师,2017(6):75-78.
[12] 胡鑫,王刚,马润年.基于动态贝叶斯网络的复杂网络抗毁性分析 [J].火力与指挥控制,2017,42(11):5-9.
[13] GAO Xiue,ZHANG Duoping,LI Keqiu,et al.A cascading failure model for command and control networks with hierarchystructure [J].Security and Communication Networks,2018,9:1-14.
[14] DEHMER M,MOWSHOWITZ A.A history of graph entropy measures [J].Inf Sci,2011,181:57-78.
[15] CAO S,DEHMER M,SHI Y.Externality of degree-based graph entropies [J].Inf Sci,2014,278:22-33.
[16] CAO S,DEHMER M.Degree-based entropies of networks revisited [J].Appl Math Comput,2015,261:141-147.
[17] ZHANG J,ZHAO C,WU H,et al.Virtual network embedding based on graph entropy [J].Entropy,2018,20(5):315-326.
[18] GHORBANI M,DEHMER M,ZANG S,et al.Graph operations based on using distance-based graph entropies [J].Applied Mathematics & Computation,2018,333:547-555.
[19] Pajek datasets [DB/OL].(2003-10-11) [2018-09-15].http://vlado.fmf.uni-lj.si/pub/networks/data/.

基于介度熵的复杂网络节点重要度识别方法

Importance identification method of complex network nodes based on betweenness and degree entropy

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