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Analyzing the structure of earthquake network by k-core decomposition

Author

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  • He, Xuan
  • Zhao, Hai
  • Cai, Wei
  • Li, Guang-Guang
  • Pei, Fan-Dong

Abstract

Earthquake network is a novel methodology to analyze the relationships among the earthquake events. k-core decomposition is one of the common measures to identify the most important nodes in complex networks. In this paper, we first verify the hierarchical structure of the earthquake network, which is constructed by the method based on space–time influence domain, and use the k-core decomposition to visualize the topology in order to figure out the structure and the central part of earthquake networks. On observation of the evolution of the maximum coreness, it is found that it has some sudden changes with the occurrence of major shocks. The highest core, which has high clustering characteristic, is tend to be located in the area that can directly or indirectly cause the major shocks. By comparing the energy distribution in each shell, we find that the energy proportion of the highest core is high, especially before the major shocks.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:421:y:2015:i:c:p:34-43
    DOI: 10.1016/j.physa.2014.11.022
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    Cited by:

    1. Angelou, Konstantinos & Maragakis, Michael & Argyrakis, Panos, 2019. "A structural analysis of the patent citation network by the k-shell decomposition method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 476-483.
    2. Xu, Yanjie & Ren, Tao & Liu, Yiyang & Li, Zhe, 2018. "Earthquake prediction based on community division," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 969-974.
    3. Zhang, Ya & Zhao, Hai & He, Xuan & Pei, Fan-Dong & Li, Guang-Guang, 2016. "Bayesian prediction of earthquake network based on space–time influence domain," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 445(C), pages 138-149.

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