Motivated by the study of food trade relationships within the European Union and the objective of uncovering similarities among food trade market networks, we employ a Dirichlet process mixture of centered Erdős-Rényi kernels for multiple network data, as introduced in [1]. The outcomes of our analysis are easily interpretable, and the clusters we identify exhibit distinct topological properties. Our approach can be alternatively interpreted as a strategy to address the challenge of reducing the number of layers with redundant information in multiplex network data.
Barile, F., Lunagomez, S., Nipoti, B. (2025). Clustering Multiple Networks Data with an Application to the EU Food Trade Market. In A. Pollice, P. Mariani (a cura di), Methodological and Applied Statistics and Demography II SIS 2024, Short Papers, Solicited Sessions (pp. 68-72). Springer Nature Switzerland [10.1007/978-3-031-64350-7_12].
Clustering Multiple Networks Data with an Application to the EU Food Trade Market
Barile, F;Nipoti, B
2025
Abstract
Motivated by the study of food trade relationships within the European Union and the objective of uncovering similarities among food trade market networks, we employ a Dirichlet process mixture of centered Erdős-Rényi kernels for multiple network data, as introduced in [1]. The outcomes of our analysis are easily interpretable, and the clusters we identify exhibit distinct topological properties. Our approach can be alternatively interpreted as a strategy to address the challenge of reducing the number of layers with redundant information in multiplex network data.
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