Identifying vital nodes in complex networks by adjacency information entropy.

Abstract:

:Identifying the vital nodes in networks is of great significance for understanding the function of nodes and the nature of networks. Many centrality indices, such as betweenness centrality (BC), eccentricity centrality (EC), closeness centricity (CC), structural holes (SH), degree centrality (DC), PageRank (PR) and eigenvector centrality (VC), have been proposed to identify the influential nodes of networks. However, some of these indices have limited application scopes. EC and CC are generally only applicable to undirected networks, while PR and VC are generally used for directed networks. To design a more applicable centrality measure, two vital node identification algorithms based on node adjacency information entropy are proposed in this paper. To validate the effectiveness and applicability of the proposed algorithms, contrast experiments are conducted with the BC, EC, CC, SH, DC, PR and VC indices in different kinds of networks. The results show that the index in this paper has a high correlation with the local metric DC, and it also has a certain correlation with the PR and VC indices for directed networks. In addition, the experimental results indicate that our algorithms can effectively identify the vital nodes in different networks.

journal_name

Sci Rep

journal_title

Scientific reports

authors

Xu X,Zhu C,Wang Q,Zhu X,Zhou Y

doi

10.1038/s41598-020-59616-w

subject

Has Abstract

pub_date

2020-02-14 00:00:00

pages

2691

issue

1

issn

2045-2322

pii

10.1038/s41598-020-59616-w

journal_volume

10

pub_type

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