Identifying Influential Nodes of Complex Networks Based on Trustvalue
WANG Bin1,WANG Yayun1,SHENG Jinfang1,SUN Zejun1,2
1School of Computer Science and Engineering,Central South University,Changsha 410083,China 2Department of Network Center,Pingdingshan University,Pingdingshan 467000,China
Abstract：Identifying influential nodes of complex networks means a lot to comprehend the structure and function,PageRank has achieved excellent results based on the nonstructural 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 similarityratio and the degreeratio that trustvalue derives from are putted forward.The nodes with higher trustvalue will get more vote.Then the TrustPageRankTPRis 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.
王斌,王亚云,盛津芳,孙泽军,. 基于节点信任度的复杂网络关键节点识别[J]. 小型微型计算机系统, 2019, 40(11): 2337-2342.
WANG Bin,WANG Yayun,SHENG Jinfang,SUN Zejun,. Identifying Influential Nodes of Complex Networks Based on Trustvalue. Journal of Chinese Computer Systems, 2019, 40(11): 2337-2342.
2019, Vol. 40 
