Detecting Anomalies in European Trade Data Using Directed Weighted Multilayer Dynamic Networks

Abstract Finding clusters in a dynamic trade network and identifying anomalies in the trading pattern is a problem of great interest. In this paper, a novel method of clustering dynamic multilayer weighted network relying on a mixture of tensor normal distribution has been proposed. Upon finding the clusters of nodes exhibiting similar trading relationships over the years, atypical trading exchanges have been identified that deviate from the regular pattern. The developed methodology has been applied to a European trade network consisting of 39 countries observed over a decade.