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Quantifying the Robustness of Complex Networks with Heterogeneous Nodes

Author

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  • Prasan Ratnayake

    (Department of Physics, Faculty of Science, University of Colombo, Colombo 00700, Sri Lanka)

  • Sugandima Weragoda

    (Department of Physics, Faculty of Science, University of Colombo, Colombo 00700, Sri Lanka)

  • Janaka Wansapura

    (Department of Physics, Faculty of Science, University of Colombo, Colombo 00700, Sri Lanka)

  • Dharshana Kasthurirathna

    (Faculty of Computing, Sri Lanka Institute of Information Technology, B263, Malabe 10115, Sri Lanka)

  • Mahendra Piraveenan

    (Complex Systems Research Group, Faculty of Engineering, University of Sydney, Camperdown, NSW 2006, Australia)

Abstract

The robustness of a complex network measures its ability to withstand random or targeted attacks. Most network robustness measures operate under the assumption that the nodes in a network are homogeneous and abstract. However, most real-world networks consist of nodes that are heterogeneous in nature. In this work, we propose a robustness measure called fitness-incorporated average network efficiency, that attempts to capture the heterogeneity of nodes using the ‘fitness’ of nodes in measuring the robustness of a network. Further, we adopt the same measure to compare the robustness of networks with heterogeneous nodes under varying topologies, such as the scale-free topology or the Erdős–Rényi random topology. We apply the proposed robustness measure using a wireless sensor network simulator to show that it can be effectively used to measure the robustness of a network using a topological approach. We also apply the proposed robustness measure to two real-world networks; namely the C O 2 exchange network and an air traffic network. We conclude that with the proposed measure, not only the topological structure, but also the fitness function and the fitness distribution among nodes, should be considered in evaluating the robustness of a complex network.

Suggested Citation

  • Prasan Ratnayake & Sugandima Weragoda & Janaka Wansapura & Dharshana Kasthurirathna & Mahendra Piraveenan, 2021. "Quantifying the Robustness of Complex Networks with Heterogeneous Nodes," Mathematics, MDPI, vol. 9(21), pages 1-20, November.
  • Handle: RePEc:gam:jmathe:v:9:y:2021:i:21:p:2769-:d:669964
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    References listed on IDEAS

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    1. Wang, Bing & Tang, Huanwen & Guo, Chonghui & Xiu, Zhilong & Zhou, Tao, 2006. "Optimization of network structure to random failures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 368(2), pages 607-614.
    2. Crucitti, Paolo & Latora, Vito & Marchiori, Massimo & Rapisarda, Andrea, 2004. "Error and attack tolerance of complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 340(1), pages 388-394.
    3. Jianxi Gao & Baruch Barzel & Albert-László Barabási, 2016. "Erratum: Universal resilience patterns in complex networks," Nature, Nature, vol. 536(7615), pages 238-238, August.
    4. Simone Caschili & Aura Reggiani & Francesca Medda, 2015. "Resilience and Vulnerability of Spatial Economic Networks," Networks and Spatial Economics, Springer, vol. 15(2), pages 205-210, June.
    5. Jianxi Gao & Baruch Barzel & Albert-László Barabási, 2016. "Universal resilience patterns in complex networks," Nature, Nature, vol. 530(7590), pages 307-312, February.
    6. Réka Albert & Hawoong Jeong & Albert-László Barabási, 2000. "Error and attack tolerance of complex networks," Nature, Nature, vol. 406(6794), pages 378-382, July.
    7. Khanh Nguyen & Duc A. Tran, 2012. "Fitness-Based Generative Models for Power-Law Networks," Springer Optimization and Its Applications, in: My T. Thai & Panos M. Pardalos (ed.), Handbook of Optimization in Complex Networks, edition 1, chapter 0, pages 39-53, Springer.
    8. Supun S. Perera & Michael G. H. Bell & Mahendrarajah Piraveenan & Dharshana Kasthurirathna & Mamata Parhi, 2018. "Topological Structure of Manufacturing Industry Supply Chain Networks," Complexity, Hindawi, vol. 2018, pages 1-23, October.
    9. Li, Jie & Wang, Juan & Sun, Shiwen & Xia, Chengyi, 2018. "Cascading crashes induced by the individual heterogeneity in complex networks," Applied Mathematics and Computation, Elsevier, vol. 323(C), pages 182-192.
    10. Ilaria Giannoccaro & Vito Albino & Anand Nair, 2018. "Advances on the Resilience of Complex Networks," Complexity, Hindawi, vol. 2018, pages 1-3, August.
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    Cited by:

    1. Wang, Shuliang & Dong, Qiqi, 2023. "A multi-source power grid's resilience enhancement strategy based on subnet division and power dispatch," International Journal of Critical Infrastructure Protection, Elsevier, vol. 41(C).

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