Finite data-size scaling of clustering in earthquake networks

TL;DR

This paper discovers a universal finite data-size scaling law for the clustering coefficient in earthquake networks, demonstrating how network characteristics depend on data size and scale, supported by analysis of datasets from California, Japan, and Iran.

Contribution

It introduces the concept of finite data-size scaling for earthquake networks and shows its universality across different regions and magnitude thresholds.

Findings

Identified a universal scaling law for clustering coefficient.

Demonstrated dependence of network metrics on data size and scale.

Supported findings with datasets from three different seismic regions.

Abstract

Earthquake network is known to be of the small-world type. The values of the network characteristics, however, depend not only on the cell size (i.e., the scale of coarse graining needed for constructing the network) but also on the size of a seismic data set. Here, discovery of a scaling law for the clustering coefficient in terms of the data size, which is refereed to here as finite data-size scaling, is reported. Its universality is shown to be supported by the detailed analysis of the data taken from California, Japan and Iran. Effects of setting threshold of magnitude are also discussed.