基于节点信任度的复杂网络关键节点识别  

Identifying Influential Nodes of Complex Networks Based on Trust-value

作　　者：王斌[1] 王亚云 盛津芳[1] 孙泽军 WANG Bin;WANG Ya-yun;SHENG Jin-fang;SUN Ze-jun(School of Computer Science and Engineering,Central South University,Changsha 410083,China;Department of Network Center,Pingdingshan University,Pingdingshan 467000,China)

机构地区：[1]中南大学计算机学院,长沙410083 [2]平顶山学院网络中心,河南平顶山467000

出　　处：《小型微型计算机系统》2019年第11期2337-2342,共6页Journal of Chinese Computer Systems

基　　金：国家科技重大专项项目(2017ZX06002005)资助

摘　　要：识别复杂网络中的关键节点对理解网络结构及功能有重要意义,PageRank算法基于网络非结构信息,在识别关键节点方面取得了很好的成效,但PageRank算法采用平均分配策略,即将节点的PageRank值平均分配给相邻节点,与实际认知存在偏差.本文考虑网络的结构及属性信息提出节点相似性比例和相邻度比例,进而提出节点信任度,网络中信任度值越大的相邻节点可以获得更多的贡献值.将节点信任度引入到PageRank算法中,构建了一种关键节点识别算法TPR(Trust-PageRank).实验部分选取真实网络利用SIR传染病模型进行评价,将TPR与度中心性,介数中心性,PageRank,HITS算法结果进行对比,实验结果表明该算法能合理有效地识别关键节点,并且在SIR初始传播和识别重要度相当的节点时有一定优势.Identifying influential nodes of complex networks means a lot to comprehend the structure and function,PageRank has achieved excellent results based on the non-structural information of networks whereas PageRank applies an average contribution strategy,which distributes the PageRank values of nodes to adjacent nodes evenly,and that deviated from the actual cognition. In this paper,the structure and property in networks are considered,therefore,the similarity-ratio and the degree-ratio that trust-value derives from are putted forward. The nodes with higher trust-value will get more vote. Then the Trust-PageRank( TPR) is comprehensively proposed to identify influential nodes in complex networks. Finally,several real networks are selected for verification using SIR model. Compared with Degree Centrality,Betweenness Centrality,PageRank and HITS,TPR is rational and effective demonstrated by the results of experiments,and has a plus at the initial propagation of SIR and the identification of nodes with close influence.

关 键 词：相似性 节点信任度 PAGERANK 关键节点 复杂网络 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]