中国物理B ›› 2023, Vol. 32 ›› Issue (9): 98905-098905.doi: 10.1088/1674-1056/acd9c3

• • 上一篇    

Robustness of community networks against cascading failures with heterogeneous redistribution strategies

Bo Song(宋波)1, Hui-Ming Wu(吴惠明)2, Yu-Rong Song(宋玉蓉)2,†, Guo-Ping Jiang(蒋国平)2, Ling-Ling Xia(夏玲玲)3, and Xu Wang(王旭)4   

  1. 1 School of Modern Posts, Nanjing University of Post and Telecommunication, Nanjing 210023, China;
    2 College of Automation & College of Artificial Intelligence, Nanjing University of Post and Telecommunication, Nanjing 210023, China;
    3 Department of Computer Information and Cyber Security, Jiangsu Police Institute, Nanjing 210023, China;
    4 GBDTC, University of Technology Sydney, Sydney, NSW 2007, Australia
  • 收稿日期:2023-03-03 修回日期:2023-04-28 接受日期:2023-05-30 发布日期:2023-08-28
  • 通讯作者: Yu-Rong Song E-mail:songyr@njupt.edu.cn
  • 基金资助:
    Project supported by the National Natural Science Foundation of China (Grant Nos. 62203229, 61672298, 61873326, and 61802155), the Philosophy and Social Sciences Research of Universities in Jiangsu Province (Grant No. 2018SJZDI142), the Natural Science Research Projects of Universities in Jiangsu Province (Grant No. 20KJB120007), the Jiangsu Natural Science Foundation Youth Fund Project (Grant No. BK20200758), Qing Lan Project and the Science and Technology Project of Market Supervision Administration of Jiangsu Province (Grant No. KJ21125027).

Robustness of community networks against cascading failures with heterogeneous redistribution strategies

Bo Song(宋波)1, Hui-Ming Wu(吴惠明)2, Yu-Rong Song(宋玉蓉)2,†, Guo-Ping Jiang(蒋国平)2, Ling-Ling Xia(夏玲玲)3, and Xu Wang(王旭)4   

  1. 1 School of Modern Posts, Nanjing University of Post and Telecommunication, Nanjing 210023, China;
    2 College of Automation & College of Artificial Intelligence, Nanjing University of Post and Telecommunication, Nanjing 210023, China;
    3 Department of Computer Information and Cyber Security, Jiangsu Police Institute, Nanjing 210023, China;
    4 GBDTC, University of Technology Sydney, Sydney, NSW 2007, Australia
  • Received:2023-03-03 Revised:2023-04-28 Accepted:2023-05-30 Published:2023-08-28
  • Contact: Yu-Rong Song E-mail:songyr@njupt.edu.cn
  • Supported by:
    Project supported by the National Natural Science Foundation of China (Grant Nos. 62203229, 61672298, 61873326, and 61802155), the Philosophy and Social Sciences Research of Universities in Jiangsu Province (Grant No. 2018SJZDI142), the Natural Science Research Projects of Universities in Jiangsu Province (Grant No. 20KJB120007), the Jiangsu Natural Science Foundation Youth Fund Project (Grant No. BK20200758), Qing Lan Project and the Science and Technology Project of Market Supervision Administration of Jiangsu Province (Grant No. KJ21125027).

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摘要： Network robustness is one of the core contents of complex network security research. This paper focuses on the robustness of community networks with respect to cascading failures, considering the nodes influence and community heterogeneity. A novel node influence ranking method, community-based Clustering-LeaderRank (CCL) algorithm, is first proposed to identify influential nodes in community networks. Simulation results show that the CCL method can effectively identify the influence of nodes. Based on node influence, a new cascading failure model with heterogeneous redistribution strategy is proposed to describe and analyze node fault propagation in community networks. Analytical and numerical simulation results on cascading failure show that the community attribute has an important influence on the cascading failure process. The network robustness against cascading failures increases when the load is more distributed to neighbors of the same community instead of different communities. When the initial load distribution and the load redistribution strategy based on the node influence are the same, the network shows better robustness against node failure.

关键词: community networks, cascading failure model, network robustness, nodes influence identification

Abstract: Network robustness is one of the core contents of complex network security research. This paper focuses on the robustness of community networks with respect to cascading failures, considering the nodes influence and community heterogeneity. A novel node influence ranking method, community-based Clustering-LeaderRank (CCL) algorithm, is first proposed to identify influential nodes in community networks. Simulation results show that the CCL method can effectively identify the influence of nodes. Based on node influence, a new cascading failure model with heterogeneous redistribution strategy is proposed to describe and analyze node fault propagation in community networks. Analytical and numerical simulation results on cascading failure show that the community attribute has an important influence on the cascading failure process. The network robustness against cascading failures increases when the load is more distributed to neighbors of the same community instead of different communities. When the initial load distribution and the load redistribution strategy based on the node influence are the same, the network shows better robustness against node failure.

Key words: community networks, cascading failure model, network robustness, nodes influence identification

中图分类号:  (Complex systems)

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