Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/82257
Title: | Critical nodes identification in complex networks | Authors: | Yang, H An, S |
Issue Date: | 2020 | Source: | Symmetry, Jan. 2020, v. 12, no. 1, 123, p. 1-14 | Abstract: | Critical nodes identification in complex networks is significance for studying the survivability and robustness of networks. The previous studies on structural hole theory uncovered that structural holes are gaps between a group of indirectly connected nodes and intermediaries that fill the holes and serve as brokers for information exchange. In this paper, we leverage the property of structural hole to design a heuristic algorithm based on local information of the network topology to identify node importance in undirected and unweighted network, whose adjacency matrix is symmetric. In the algorithm, a node with a larger degree and greater number of structural holes associated with it, achieves a higher importance ranking. Six real networks are used as test data. The experimental results show that the proposed method not only has low computational complexity, but also outperforms degree centrality, k-shell method, mapping entropy centrality, the collective influence algorithm, DDN algorithm that based on node degree and their neighbors, and random ranking method in identifying node importance for network connectivity in complex networks. | Keywords: | Network disintegration Network connectivity Node importance Structure hole |
Publisher: | MDPI | Journal: | Symmetry | EISSN: | 2073-8994 | DOI: | 10.3390/sym12010123 | Rights: | © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). The following publication Yang, H.; An, S. Critical Nodes Identification in Complex Networks. Symmetry 2020, 12, 123 is available at https://dx.doi.org/10.3390/sym12010123 |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Yang_Identification_Complex_Networks.pdf | 1.71 MB | Adobe PDF | View/Open |
Page views
29
Last Week
1
1
Last month
Citations as of May 28, 2023
Downloads
41
Citations as of May 28, 2023
SCOPUSTM
Citations
21
Citations as of May 25, 2023
WEB OF SCIENCETM
Citations
14
Citations as of Jun 1, 2023

Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.