Detecting communities and complex network features of the Italian earthquake catalog

We represent an earthquake catalog as a complex network, where each node corresponds to a seismic event, and the links between nodes represent their distances in the space-time-energy domain. This approach, commonly known as the nearest-neighbor (NN) method, was introduced by Baiesi and Paczuski in 2004 to analyze the Californian earthquake catalog. Unlike the traditional fixed window-based techniques, which group into a cluster all the events within a predefined space-time domain centered at a selected mainshock, the nearest-neighbor method offers greater flexibility and precision. In fact, the resulting weighted network is able to capture extensive information about the space-time seismicity of a given region.

In our study, we use network theory to analyze the Italian earthquake catalog from 2010 to 2020, compiled by the National Institute of Geophysics and Volcanology (INGV). Specifically, we first apply the NN method to derive the seismic Italian structure as a weighted network. Then,  we focus our analysis on its topological properties to detect communities, that are groups of nodes more tightly interconnected with each other than with the rest of the network.

To achieve this, we use the Louvain and Leiden algorithms: they are based on the maximization of modularity, a metric that quantifies the strength of a network’s division into modules (also referred to as communities or clusters). For implementation, we rely on IT tools new to seismology, but largely used in complex network analysis. Specifically, we use Radatools, an Ada library designed for analyzing complex networks, alongside the NetworkX Python package, which facilitates the creation, manipulation, and study of network structures, dynamics, and functions.

This approach enables us to identify communities in the Italian network, and to compare them with clusters derived using traditional window-based techniques commonly employed in the literature, such as the Gardner-Knopoff technique. By investigating these differences and similarities, we aim to provide a robust and comprehensive analysis of the Italian earthquake catalog, leveraging high-performance tools for studying complex seismic networks.