Constructing Weighted Networks of Earthquakes with Multiple-parent Nodes Based on Correlation-Metric

In this paper, we address a problem of constructing weighted networks of earthquakes with multiple parent nodes, where the pairs of earthquakes with strong interaction are connected. To this end, by extending a representative conventional method based on the correlation-metric that produces an unweighted network with a single-parent node, we develop a method for constructing a network with multiple-parent nodes and assigning weight to each link by a link-weighting scheme called logarithmic-inverse-distance. In our experimental evaluation, we use an earthquake catalog that covers the whole of Japan, and select 24 major earthquakes which caused significant damage or casualties in Japan. In comparison to four different link-weighting schema, i.e., uniform, magnitude, inverse-distance, and normalized-inverse-distance, we evaluate the effectiveness of the constructed networks by our proposed method, in terms of the ranking accuracy based on the most basic centrality, i.e., weighted degree measure. As a consequence, we show that our proposed method works well, and then discuss the reasons why weighted networks with multiple-parent nodes can improve the ranking accuracy.