Robustness of higher-order networks with synergistic protection

被引:6
|
作者
Chen, Qihang [1 ]
Zhao, Yang [1 ]
Li, Cong [1 ]
Li, Xiang [2 ]
机构
[1] Fudan Univ, Sch Informat Sci & Technol, Dept Elect Engn, Adapt Networks & Control Lab, Shanghai 200433, Peoples R China
[2] Tongji Univ, Inst Complex Networks & Intelligent Syst, Shanghai Res Inst Intelligent Autonomous Syst, Shanghai 201210, Peoples R China
来源
NEW JOURNAL OF PHYSICS | 2023年 / 25卷 / 11期
基金
上海市自然科学基金; 中国国家自然科学基金;
关键词
higher-order networks; percolation theory; network robustness; synergistic protection; PERCOLATION; MITIGATION;
D O I
10.1088/1367-2630/ad0a15
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
From chemical reactions to human communications, higher-order interactions are ubiquitous in real-world networks. Entities within higher-order interactions often exhibit collective behaviors that could create synergistic effects on robustness of the underlying system. Here we propose an analytical model to investigate the robustness of higher-order networks, in which potential higher-order synergistic protection is incorporated. In this model, higher-order networks are described with simplicial complexes, and robustness is studied under the proposed dynamics of extended bond percolation. We provide theoretical analysis for robustness quantities including the relative size of the giant component and percolation threshold. We discover that the percolation threshold could drop to zero, which is an indicator of notably strong robustness, with synergistic protective effects and dense higher-order simplices. We also find that higher-order interactions have strong impacts on the association between robustness and clustering. Specifically, a larger clustering coefficient could invariably indicate stronger robustness once the strength of protective effects exceeds a certain value. Our theoretical solutions are verified by simulation results in simplicial complexes with Poisson, exponential and power-law distributions.
引用
收藏
页数:15
相关论文
共 50 条
  • [41] Coevolution of epidemic and infodemic on higher-order networks
    Li, Wenyao
    Cai, Meng
    Zhong, Xiaoni
    Liu, Yanbing
    Lin, Tao
    Wang, Wei
    CHAOS SOLITONS & FRACTALS, 2023, 168
  • [42] Higher-order Clustering in Complex Heterogeneous Networks
    Carranza, Aldo G.
    Rossi, Ryan A.
    Rao, Anup
    Koh, Eunyee
    KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, 2020, : 25 - 35
  • [43] Study on the robust control of higher-order networks
    Ma, Fuxiang
    Yu, Wenqian
    Ma, Xiujuan
    SCIENTIFIC REPORTS, 2025, 15 (01):
  • [44] Functional approximation of higher-order neural networks
    City Univ of Hong Kong, Kowloon, Hong Kong
    J Intell Syst, 3-4 (239-260):
  • [45] Measuring the significance of higher-order dependency in networks
    Li, Jiaxu
    Lu, Xin
    NEW JOURNAL OF PHYSICS, 2024, 26 (03):
  • [46] HoNVis: Visualizing and Exploring Higher-Order Networks
    Tao, Jun
    Xu, Jian
    Wang, Chaoli
    Chawla, Nitesh V.
    2017 IEEE PACIFIC VISUALIZATION SYMPOSIUM (PACIFICVIS), 2017, : 1 - 10
  • [47] Topology and dynamics of higher-order multiplex networks
    Krishnagopal, Sanjukta
    Bianconi, Ginestra
    CHAOS SOLITONS & FRACTALS, 2023, 177
  • [48] Multiorder Laplacian for synchronization in higher-order networks
    Lucas, Maxime
    Cencetti, Giulia
    Battiston, Federico
    PHYSICAL REVIEW RESEARCH, 2020, 2 (03):
  • [49] Higher-Order Sensitivity Invariants for Nonlinear Networks
    Izydorczyk, J.
    Chojcan, J.
    2008 IEEE MEDITERRANEAN ELECTROTECHNICAL CONFERENCE, VOLS 1 AND 2, 2008, : 6 - +
  • [50] Scale Equalized Higher-order Neural Networks
    Lin, CM
    Wu, KH
    Wang, JH
    INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS, VOL 1-4, PROCEEDINGS, 2005, : 816 - 821