Seismic time series has been mapped as a complex network, where a geographical region is divided into square cells that represent the nodes and connections are defined according to the sequence of earthquakes. In this paper, we map a seismic time series to a temporal network, described by a multiplex network, and characterize the evolution of the network structure in terms of the eigenvector centrality measure. We generalize previous works that considered the single layer representation of earthquake networks. Our results suggest that the multiplex representation captures better earthquake activity than methods based on single layer networks. We also verify that the regions with highest seismological activities in Iran and California can be identified from the network centrality analysis. The temporal modeling of seismic data provided here may open new possibilities for a better comprehension of the physics of earthquakes.
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Centrality in earthquake multiplex networks
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June 2018
Research Article|
June 12 2018
Centrality in earthquake multiplex networks
Nastaran Lotfi
;
Nastaran Lotfi
a)
1
University of Zanjan
, 45371-38791 Zanjan, Iran
2
Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo
, Caixa Postal 668, 13560-970 São Carlos, SP, Brazil
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Amir Hossein Darooneh;
Amir Hossein Darooneh
1
University of Zanjan
, 45371-38791 Zanjan, Iran
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Francisco A. Rodrigues
Francisco A. Rodrigues
2
Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo
, Caixa Postal 668, 13560-970 São Carlos, SP, Brazil
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a)
Electronic mail: [email protected]
Chaos 28, 063113 (2018)
Article history
Received:
August 24 2017
Accepted:
May 24 2018
Citation
Nastaran Lotfi, Amir Hossein Darooneh, Francisco A. Rodrigues; Centrality in earthquake multiplex networks. Chaos 1 June 2018; 28 (6): 063113. https://doi.org/10.1063/1.5001469
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