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Targeted k-node collapse problem: Towards understanding the robustness of local k-core structure

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Listed:
  • Lv, Yuqian
  • Zhou, Bo
  • Wang, Jinhuan
  • Xuan, Qi

Abstract

The concept of k-core, which indicates the largest induced subgraph where each node has k or more neighbors, plays a significant role in measuring the cohesiveness and engagement of a network, and it is exploited in diverse applications, e.g., network analysis, anomaly detection, community detection, etc. However, recent studies have demonstrated the vulnerability of k-core under malicious perturbations which focus on removing the minimal number of edges to make k-core structures collapse. Despite this, to the best of our knowledge, no existing research has yet concentrated on the minimal number of edges that must be removed to collapse a specific node in the k-core. To address this issue, in this paper, we make the first attempt to study the robustness of individual nodes in k-core and propose the Targeted k-node Collapse Problem (TNCP) with three novel contributions. Firstly, we offer a general definition of TNCP problem with a proof of its NP-hardness. Secondly, in order to cover the TNCP problem, we propose a heuristic algorithm named TNC and its improved version named ATNC for implementations on large-scale networks. Finally, experiments on 20 real-world networks across various domains verify the superiority of our proposed algorithms over 6 baseline methods with detailed comparisons and analyses. Resource related to our study is publicly available at https://github.com/Yocenly/TNCP.

Suggested Citation

  • Lv, Yuqian & Zhou, Bo & Wang, Jinhuan & Xuan, Qi, 2024. "Targeted k-node collapse problem: Towards understanding the robustness of local k-core structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 641(C).
  • Handle: RePEc:eee:phsmap:v:641:y:2024:i:c:s0378437124002413
    DOI: 10.1016/j.physa.2024.129732
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    References listed on IDEAS

    as
    1. Huo, Long & Chen, Xin, 2022. "The waiting-time distribution for network partitions in cascading failures in power networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).
    2. Grossman, Tal & Wool, Avishai, 1997. "Computational experience with approximation algorithms for the set covering problem," European Journal of Operational Research, Elsevier, vol. 101(1), pages 81-92, August.
    3. Benatti, Alexandre & de Arruda, Henrique Ferraz & Silva, Filipi Nascimento & Comin, César Henrique & da Fontoura Costa, Luciano, 2023. "On the stability of citation networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 610(C).
    4. Al-garadi, Mohammed Ali & Varathan, Kasturi Dewi & Ravana, Sri Devi, 2017. "Identification of influential spreaders in online social networks using interaction weighted K-core decomposition method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 278-288.
    5. Wang, Wei & Li, Wenyao & Lin, Tao & Wu, Tao & Pan, Liming & Liu, Yanbing, 2022. "Generalized k-core percolation on higher-order dependent networks," Applied Mathematics and Computation, Elsevier, vol. 420(C).
    6. Bae, Joonhyun & Kim, Sangwook, 2014. "Identifying and ranking influential spreaders in complex networks by neighborhood coreness," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 395(C), pages 549-559.
    7. He, Xuan & Zhao, Hai & Cai, Wei & Li, Guang-Guang & Pei, Fan-Dong, 2015. "Analyzing the structure of earthquake network by k-core decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 34-43.
    8. Angelou, Konstantinos & Maragakis, Michael & Kosmidis, Kosmas & Argyrakis, Panos, 2020. "A hybrid model for the patent citation network structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    9. Miralles, Alicia & Comellas, Francesc & Chen, Lichao & Zhang, Zhongzhi, 2010. "Planar unclustered scale-free graphs as models for technological and biological networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(9), pages 1955-1964.
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