Critical node identification for complex network based on a novel minimum connected dominating set
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Identifying critical nodes in complex networks aims to fragment a graph \(G = (V, E)\) by removing a set of vertices R with cardinality \(\left| R \right| \le \) k, such that the residual graph has minimum pairwise connectivity. Existing optimization algorithms are incapable of finding a good set R in complex networks. By investigating the role of nodes, a minimum dominating set approach is considered in controlling a network. This paper presents an algorithmic procedure to compute the critical nodes using a novel minimum connected dominating set, in which the critical nodes are identified based on the number of close subsequences. Through experimental verification on some randomly generated networks and comparing with the similar algorithms, the results showed that the proposed algorithm has high capability of identifying the critical nodes and low time complexity.
KeywordsMinimum connected dominating set Critical nodes identification The number of close subsequences
This research is supported by the financial support from Zhejiang Provincial Natural Science Foundation of China under Grant Nos. LY16F020027, LZ15F030004 and LY15F020040, Zhejiang Provincial Education Department Research Foundation of China under Grant No.Y201533771, Humanity and Social Science Youth foundation of Ministry of Education of China under Grant No. 15YJCZH088, National Nature Science Foundation of China under Grant Nos. 61272020 and 61370185, and Natural Science Foundation of Jiangxi Province under Grant No. 20151BAB217008.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.
Human and animal rights statement
This article does not contain any studies with animals performed by any of the authors
Informed consent was obtained from all individual participants included in the study.
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