AN IMPROVED PAGERANK FOR IDENTIFYING THE INFLUENTIAL NODES BASED ON RESOURCE ALLOCATION IN DIRECTED NETWORKS

被引：0

作者：

Zhong, Linfeng ^{[1
,2
]}

Lv, Fengmao ^{[2
]}

机构：

[1] Univ Elect Sci & Technol China, Web Sci Ctr, Chengdu 611731, Sichuan, Peoples R China

[2] Univ Elect Sci & Technol China, Big Data Res Ctr, Chengdu 611731, Sichuan, Peoples R China

来源：

2017 14TH INTERNATIONAL COMPUTER CONFERENCE ON WAVELET ACTIVE MEDIA TECHNOLOGY AND INFORMATION PROCESSING (ICCWAMTIP) | 2017年

基金：

中国国家自然科学基金;

关键词：

Directed Networks; Pagerank; Resource Allocation; Susceptible Infected Recovered; Kendall; COMPLEX; CENTRALITY;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Identifying the influential nodes in directed networks is one of the most promising domains. In this paper, we present an improved PageRank based on the resource allocation (IPRA) method to identify the influential nodes. Comparing with the results of the Susceptible Infected Recovered (SIR) model for four real networks, the IPRA method could identify influential nodes more accurately than the PageRank and ClusterRank. Specially, in the US air line network, the Kendall's tau could be enhanced 700% when the spreading rate is 0.2. In the Email network, the Kendall's tau could be enhanced 850% when the spreading rate is 0.01.

引用

页码：42 / 45

页数：4

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