Multiplex Dirichlet stochastic block model for clustering multidimensional compositional networks

TL;DR

This paper introduces a novel multiplex Dirichlet stochastic block model that effectively clusters multidimensional compositional networks by transforming edge weights and jointly analyzing multiple interaction types.

Contribution

It proposes a new model for clustering multiplex networks with compositional data, addressing biases from raw weights and enabling joint analysis of different relation types.

Findings

Model performs well in simulation studies.

Applied successfully to international export data.

Improves clustering accuracy over traditional methods.

Abstract

Network data often represent multiple types of relations, which can also denote exchanged quantities, and are typically encompassed in a weighted multiplex. Such data frequently exhibit clustering structures, however, traditional clustering methods are not well-suited for multiplex networks. Additionally, standard methods treat edge weights in their raw form, potentially biasing clustering towards a node's total weight capacity rather than reflecting cluster-related interaction patterns. To address this, we propose transforming edge weights into a compositional format, enabling the analysis of connection strengths in relative terms and removing the impact of nodes' total weights. We introduce a multiplex Dirichlet stochastic block model designed for multiplex networks with compositional layers. This model accounts for sparse compositional networks and enables joint clustering across different types of interactions. We validate the model through a simulation study and apply it to the international export data from the Food and Agriculture Organization of the United Nations.