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    Comprehensive Degree Based Key Node Recognition Method in Complex Networks

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    Comprehensive Degree Based Key ...
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    Author
    Xie, Lixia
    Sun, Honghong
    Yang, Hongyu
    Zhang, Liang
    Affiliation
    School of Information, University of Arizona
    Issue Date
    2021-09-17
    Keywords
    Complex networks
    Comprehensive Degree
    K-shell
    Neighboring nodes
    Node importance
    
    Metadata
    Show full item record
    Publisher
    Springer International Publishing
    Citation
    Xie, L., Sun, H., Yang, H., & Zhang, L. (2021). Comprehensive Degree Based Key Node Recognition Method in Complex Networks. International Conference on Information and Communications Security 2021.
    Journal
    International Conference on Information and Communications Security 2021
    Rights
    © Springer Nature Switzerland AG 2021.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    Aiming at the problem of the insufficient resolution and accuracy of the key node recognition methods in complex networks, a Comprehensive Degree Based Key Node Recognition Method (CDKNR) in complex networks is proposed. Firstly, the K-shell method is adopted to layer the network and obtain the K-shell (Ks) value of each node, and the influence of the global structure of the network is measured by the Ks value. Secondly, the concept of Comprehensive Degree (CD) is proposed, and a dynamically adjustable influence coefficient μi is set, and the Comprehensive Degree of each node is obtained by measuring the influence of the local structure of the network through the number of neighboring nodes and sub-neighboring nodes and influence coefficient μi. Finally, the importance of nodes is distinguished according to the Comprehensive Degree. Compared with several classical methods and risk assessment method, the experimental results show that the proposed method can effectively identify the key nodes, and has high accuracy and resolution in different complex networks. In addition, the CDKNR can provide a basis for risk assessment of network nodes, important node protection and risk disposal priority ranking of nodes in the network.
    Note
    12 month embargo; first online: 17 September 2021
    ISSN
    0302-9743
    EISSN
    1611-3349
    DOI
    10.1007/978-3-030-86890-1_20
    Version
    Final accepted manuscript
    ae974a485f413a2113503eed53cd6c53
    10.1007/978-3-030-86890-1_20
    Scopus Count
    Collections
    UA Faculty Publications

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