Identifying critical nodes' group in complex networks

被引:22
|
作者
Jiang, Zhong-Yuan [1 ,2 ]
Zeng, Yong [1 ,2 ]
Liu, Zhi-Hong [1 ,2 ]
Ma, Jian-Feng [1 ,2 ]
机构
[1] Xidian Univ, Sch Cyber Engn, Xian 710071, Shaanxi, Peoples R China
[2] Xidian Univ, Shaanxi Key Lab Network & Syst Secur, Xian 710071, Shaanxi, Peoples R China
基金
中国国家自然科学基金;
关键词
Key node; Target attack; Network vulnerability; Complex network; CENTRALITY; ROBUSTNESS; FAILURES;
D O I
10.1016/j.physa.2018.09.069
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
Recently, network vulnerability or security has attracted much attention in various networked systems, and especially in security related attacks or protections, there are a set of influential nodes that can remarkably break the network connectivity. In this work, we firstly present eight attack mechanisms including target attack, random failure, betweenness based attack, closeness based attack, PageRank based attack, k-shell based attack, greedy algorithm, and low-degree attack. Secondly, inspired by the dynamic node removal process, we propose to recalculate the metrics for every node removal strategy, and evaluate the network robustness against all these heuristic attack strategies with and without recalculations in scale-free networks, random networks, and many real network models. The simulations indicate that most of the attack strategies with recalculations appear to imperil the network structure security more. Furthermore, considering that key node set mining is very critical for network structure protections, we employ minimum number of key nodes (MNKN) metric to further discuss the network vulnerability against all the attack strategies with or without recalculations. The results show that the critical nodes' group can be more efficiently found under the PageRank based attack with recalculations than under other attack disciplines with or without recalculations in most of the classic and real network models. This work investigates network structure vulnerability and security from a new perspective, and has potential applications into network structure protection or planning. (C) 2018 Elsevier B.V. All rights reserved.
引用
收藏
页码:121 / 132
页数:12
相关论文
共 50 条
  • [31] Rapid identifying high-influence nodes in complex networks
    Song Bo
    Jiang Guo-Ping
    Song Yu-Rong
    Xia Ling-Ling
    [J]. CHINESE PHYSICS B, 2015, 24 (10)
  • [32] A new evidential methodology of identifying influential nodes in complex networks
    Bian, Tian
    Deng, Yong
    [J]. CHAOS SOLITONS & FRACTALS, 2017, 103 : 101 - 110
  • [33] A neural diffusion model for identifying influential nodes in complex networks
    Ahmad, Waseem
    Wang, Bang
    [J]. Chaos, Solitons and Fractals, 2024, 189
  • [34] Identifying influential nodes in complex networks based on Neighbours and edges
    Shao, Zengzhen
    Liu, Shulei
    Zhao, Yanyu
    Liu, Yanxiu
    [J]. PEER-TO-PEER NETWORKING AND APPLICATIONS, 2019, 12 (06) : 1528 - 1537
  • [35] Rapid identifying high-influence nodes in complex networks
    宋波
    蒋国平
    宋玉蓉
    夏玲玲
    [J]. Chinese Physics B, 2015, 24 (10) : 5 - 13
  • [36] Identifying influential nodes in complex networks based on Neighbours and edges
    Zengzhen Shao
    Shulei Liu
    Yanyu Zhao
    Yanxiu Liu
    [J]. Peer-to-Peer Networking and Applications, 2019, 12 : 1528 - 1537
  • [37] Identifying influential nodes in complex networks based on expansion factor
    Liu, Dong
    Jing, Yun
    Chang, Baofang
    [J]. INTERNATIONAL JOURNAL OF MODERN PHYSICS C, 2016, 27 (09):
  • [38] Identifying vital nodes in complex networks by adjacency information entropy
    Xiang Xu
    Cheng Zhu
    Qingyong Wang
    Xianqiang Zhu
    Yun Zhou
    [J]. Scientific Reports, 10
  • [39] Identifying influential nodes in complex networks from global perspective
    Zhao, Jie
    Wang, Yunchuan
    Deng, Yong
    [J]. CHAOS SOLITONS & FRACTALS, 2020, 133
  • [40] Identifying source of an information in complex networks with limited observation nodes
    Lu, Xindai
    Yao, Yiyang
    Zhou, Yinzuo
    [J]. PROCEEDINGS OF THE 36TH CHINESE CONTROL CONFERENCE (CCC 2017), 2017, : 1456 - 1461