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Articulation points in complex networks

Author

Listed:
  • Liang Tian

    (Brigham and Women’s Hospital and Harvard Medical School
    College of Science, Nanjing University of Aeronautics and Astronautics)

  • Amir Bashan

    (Bar-Ilan University)

  • Da-Ning Shi

    (College of Science, Nanjing University of Aeronautics and Astronautics)

  • Yang-Yu Liu

    (Brigham and Women’s Hospital and Harvard Medical School
    Center for Cancer Systems Biology, Dana Farber Cancer Institute)

Abstract

An articulation point in a network is a node whose removal disconnects the network. Those nodes play key roles in ensuring connectivity of many real-world networks, from infrastructure networks to protein interaction networks and terrorist communication networks. Despite their fundamental importance, a general framework of studying articulation points in complex networks is lacking. Here we develop analytical tools to study key issues pertinent to articulation points, such as the expected number of them and the network vulnerability against their removal, in an arbitrary complex network. We find that a greedy articulation point removal process provides us a different perspective on the organizational principles of complex networks. Moreover, this process results in a rich phase diagram with two fundamentally different types of percolation transitions. Our results shed light on the design of more resilient infrastructure networks and the effective destruction of terrorist communication networks.

Suggested Citation

  • Liang Tian & Amir Bashan & Da-Ning Shi & Yang-Yu Liu, 2017. "Articulation points in complex networks," Nature Communications, Nature, vol. 8(1), pages 1-9, April.
    • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_ncomms14223
    DOI: 10.1038/ncomms14223
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    Cited by:

    1. Li, Sheng & Liu, Wenwen & Wu, Ruizi & Li, Junli, 2023. "An adaptive attack model to network controllability," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    2. Wandelt, Sebastian & Shi, Xing & Sun, Xiaoqian, 2021. "Estimation and improvement of transportation network robustness by exploiting communities," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
    3. Xiao-Long Ren & Niels Gleinig & Dijana Tolić & Nino Antulov-Fantulin, 2018. "Underestimated Cost of Targeted Attacks on Complex Networks," Complexity, Hindawi, vol. 2018, pages 1-15, January.
    4. Wandelt, Sebastian & Lin, Wei & Sun, Xiaoqian & Zanin, Massimiliano, 2022. "From random failures to targeted attacks in network dismantling," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    5. Peng, Cheng & Liu, Xiaoqi & Kang, Rui & Wang, Sihan & Gao, Shang, 2023. "Stochastic input-to-state stability for stochastic complex dynamical control networks with impulsive perturbation," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    6. Feng, Xiao & He, Shiwei & Li, Guangye & Chi, Jushang, 2021. "Transfer network of high-speed rail and aviation: Structure and critical components," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    7. Lekha, Divya Sindhu & Balakrishnan, Kannan, 2020. "Central attacks in complex networks: A revisit with new fallback strategy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    8. Tomassini, Marco, 2023. "Rewiring or adding links: A real-world case study of network vulnerability," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).

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