Centrality in earthquake multiplex networks

Area of Science:

• Geophysics
• Complex Networks
• Time Series Analysis

Background:

• Seismic time series are often mapped to complex networks using geographical cells as nodes.
• Previous methods primarily used single-layer network representations for earthquake data.

Purpose of the Study:

• To map seismic time series to a temporal multiplex network.
• To analyze the evolution of network structure using eigenvector centrality.
• To improve the representation of earthquake activity compared to single-layer models.

Main Methods:

• Representing seismic time series as a temporal multiplex network.
• Applying eigenvector centrality to characterize network structure evolution.
• Generalizing previous single-layer earthquake network models.

Main Results:

• The multiplex network representation more effectively captures seismic activity than single-layer networks.
• Eigenvector centrality analysis successfully identified high seismological activity zones in Iran and California.
• Temporal network modeling offers new insights into earthquake physics.

Conclusions:

• Multiplex temporal networks provide a superior framework for analyzing seismic data.
• Network centrality analysis is a viable tool for identifying seismic hotspots.
• This temporal modeling approach advances the understanding of earthquake dynamics.