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

Shuchismita Sarkar () and Volodymyr Melnykov ()
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Shuchismita Sarkar: Bowling Green State University
Volodymyr Melnykov: The University of Alabama

Journal of Classification, 2025, vol. 42, issue 3, No 4, 544-564

Abstract: 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.

Keywords: Finite mixture model; Model-based clustering; Directed network; Multilayer network; Dynamic network; MCMC (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s00357-025-09502-9

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