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Dynamical evolution of clustering in complex network of earthquakes

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  • S. Abe
  • N. Suzuki

Abstract

The network approach plays a distinguished role in contemporary science of complex systems/phenomena. Such an approach has been introduced into seismology in a recent work [S. Abe, N. Suzuki, Europhys. Lett. 65, 581 (2004)]. Here, we discuss the dynamical property of the earthquake network constructed in California and report the discovery that the values of the clustering coefficient remain stationary before main shocks, suddenly jump up at the main shocks, and then slowly decay following a power law to become stationary again. Thus, the dynamical network approach characterizes main shocks in a peculiar manner. Copyright EDP Sciences/Società Italiana di Fisica/Springer-Verlag 2007

Suggested Citation

  • S. Abe & N. Suzuki, 2007. "Dynamical evolution of clustering in complex network of earthquakes," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 59(1), pages 93-97, September.
    • Handle: RePEc:spr:eurphb:v:59:y:2007:i:1:p:93-97
    DOI: 10.1140/epjb/e2007-00259-3
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    References listed on IDEAS

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    1. Christos Pitelis & Roger Sugden & James R. Wilson, 2006. "Introduction," Chapters, in: Christos Pitelis & Roger Sugden & James R. Wilson (ed.), Clusters and Globalisation, chapter 1, Edward Elgar Publishing.
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    Cited by:

    1. G.-Fivos Sargentis & Theano Iliopoulou & Stavroula Sigourou & Panayiotis Dimitriadis & Demetris Koutsoyiannis, 2020. "Evolution of Clustering Quantified by a Stochastic Method—Case Studies on Natural and Human Social Structures," Sustainability, MDPI, vol. 12(19), pages 1-22, September.
    2. Ferreira, Douglas S.R. & Ribeiro, Jennifer & Oliveira, Paulo S.L. & Pimenta, André R. & Freitas, Renato P. & Papa, Andrés R.R., 2020. "Long-range correlation studies in deep earthquakes global series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).
    3. D. Chorozoglou & E. Papadimitriou, 2020. "Investigation of earthquake recurrence networks: the cases of 2014 and 2015 aftershock sequences in Ionian Islands, Greece," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 102(3), pages 783-805, July.
    4. Lotfi, Nastaran & Darooneh, Amir H., 2013. "Nonextensivity measure for earthquake networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(14), pages 3061-3065.
    5. 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.
    6. Rezaei, Soghra & Moghaddasi, Hanieh & Darooneh, Amir Hossein, 2018. "Preferential attachment in evolutionary earthquake networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 495(C), pages 172-179.
    7. Rezaei, Soghra & Darooneh, Amir Hossein & Lotfi, Nastaran & Asaadi, Nazila, 2017. "The earthquakes network: Retrieving the empirical seismological laws," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 80-87.
    8. Deyasi, Krishanu & Chakraborty, Abhijit & Banerjee, Anirban, 2017. "Network similarity and statistical analysis of earthquake seismic data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 481(C), pages 224-234.
    9. 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.

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