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Characterizing and predicting the robustness of power-law networks

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  • LaRocca, Sarah
  • Guikema, Seth D.

Abstract

Power-law networks such as the Internet, terrorist cells, species relationships, and cellular metabolic interactions are susceptible to node failures, yet maintaining network connectivity is essential for network functionality. Disconnection of the network leads to fragmentation and, in some cases, collapse of the underlying system. However, the influences of the topology of networks on their ability to withstand node failures are poorly understood. Based on a study of the response of 2000 randomly-generated power-law networks to node failures, we find that networks with higher nodal degree and clustering coefficient, lower betweenness centrality, and lower variability in path length and clustering coefficient maintain their cohesion better during such events. We also find that network robustness, i.e., the ability to withstand node failures, can be accurately predicted a priori for power-law networks across many fields. These results provide a basis for designing new, more robust networks, improving the robustness of existing networks such as the Internet and cellular metabolic pathways, and efficiently degrading networks such as terrorist cells.

Suggested Citation

  • LaRocca, Sarah & Guikema, Seth D., 2015. "Characterizing and predicting the robustness of power-law networks," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 157-166.
  • Handle: RePEc:eee:reensy:v:133:y:2015:i:c:p:157-166
    DOI: 10.1016/j.ress.2014.07.023
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    Cited by:

    1. Johnson, Caroline A. & Flage, Roger & Guikema, Seth D., 2019. "Characterising the robustness of coupled power-law networks," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    2. Zhang, Haihong & Wu, Wenqing & Zhao, Liming, 2016. "A study of knowledge supernetworks and network robustness in different business incubators," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 545-560.
    3. Johnson, Caroline A. & Flage, Roger & Guikema, Seth D., 2021. "Feasibility study of PRA for critical infrastructure risk analysis," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    4. Mukherjee, Sayanti & Nateghi, Roshanak & Hastak, Makarand, 2018. "A multi-hazard approach to assess severe weather-induced major power outage risks in the U.S," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 283-305.
    5. Wang, Ning & Gao, Ying & He, Jia-tao & Yang, Jun, 2022. "Robustness evaluation of the air cargo network considering node importance and attack cost," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    6. Dui, Hongyan & Meng, Xueyu & Xiao, Hui & Guo, Jianjun, 2020. "Analysis of the cascading failure for scale-free networks based on a multi-strategy evolutionary game," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    7. Bistouni, Fathollah & Jahanshahi, Mohsen, 2015. "Evaluating failure rate of fault-tolerant multistage interconnection networks using Weibull life distribution," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 128-146.
    8. Meng, Xueyu & Cai, Zhiqiang & Si, Shubin & Duan, Dongli, 2021. "Analysis of epidemic vaccination strategies on heterogeneous networks: Based on SEIRV model and evolutionary game," Applied Mathematics and Computation, Elsevier, vol. 403(C).
    9. Caroline A Johnson & Allison C Reilly & Roger Flage & Seth D Guikema, 2021. "Characterizing the robustness of power-law networks that experience spatially-correlated failures," Journal of Risk and Reliability, , vol. 235(3), pages 403-415, June.

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    Keywords

    Networks; Scale-free; Robustness;
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