Identifying Important Nodes in Complex Networks Based on Node Propagation Entropy.

Yong Yu,Tao Gao,Jinzhuo Liu,Biao Zhou,Linjie Chen

doi:10.3390/e24020275

Abstract

In recent years, the identification of the essential nodes in complex networks has attracted significant attention because of their theoretical and practical significance in many applications, such as preventing and controlling epidemic diseases and discovering essential proteins. Several importance measures have been proposed from diverse perspectives to identify crucial nodes more accurately. In this paper, we propose a novel importance metric called node propagation entropy, which uses a combination of the clustering coefficients of nodes and the influence of the first- and second-order neighbor numbers on node importance to identify essential nodes from an entropy perspective while considering the local and global information of the network. Furthermore, the susceptible–infected–removed and susceptible–infected–removed–susceptible epidemic models along with the Kendall coefficient are used to reveal the relevant correlations among the various importance measures. The results of experiments conducted on several real networks from different domains show that the proposed metric is more accurate and stable in identifying significant nodes than many existing techniques, including degree centrality, betweenness centrality, closeness centrality, eigenvector centrality, and H-index.

Highlights

Complex systems in many real-world domains are modeled as complex networks to ensure efficient analysis
We propose the node propagation entropy metric to quantify the importance of nodes by calculating node centrality from an entropy perspective
To better demonstrate the validity of the node propagation entropy (PE) metric for representing the importance of nodes, we evaluated it on twelve real networks from different domains

Summary

Introduction

Complex systems in many real-world domains are modeled as complex networks to ensure efficient analysis. Several importance metrics have been proposed to evaluate the significance of nodes from a network topology perspective They include degree centrality [13], betweenness centrality [14], closeness centrality [14], k-shell [15], and eigenvector centrality [16]. Degree centrality, which asserts that the more neighbors a node has, the more influential it is, is relatively simple, intuitive, and easy to implement. It generally does not consider the global properties of the network or the individual properties of nodes. Closeness centrality asserts that the smaller the mean value of the shortest length path from a node to the rest of the nodes in the network, the more influential the node is in the network

Methods

Results

Conclusion