NDLI: Identifying Important Nodes in Complex Networks Based on Node Propagation Entropy

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Identifying Important Nodes in Complex Networks Based on Node Propagation Entropy

Content Provider	MDPI
Author	Yu, Yong Zhou, Biao Chen, Linjie Gao, Tao Liu, Jinzhuo
Copyright Year	2022
Description	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.
Starting Page	275
e-ISSN	10994300
DOI	10.3390/e24020275
Journal	Entropy
Issue Number	2
Volume Number	24
Language	English
Publisher	MDPI
Publisher Date	2022-02-14
Access Restriction	Open
Subject Keyword	Entropy Information and Library Science Complex Networks Important Nodes Node Propagation Entropy Epidemic Models Importance Metric
Content Type	Text
Resource Type	Article

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