Modelling heterogeneity in Latent Space Models for Multidimensional Networks

TL;DR

This paper introduces a flexible latent space modeling framework for multidimensional networks that accounts for heterogeneity in node features and relations, improving understanding of complex network structures.

Contribution

It develops a novel class of latent space models that handle heterogeneity within and across networks, estimated with MCMC, and demonstrates their effectiveness on real data.

Findings

Models effectively distinguish node-specific features and relations.

Simulation studies show accurate parameter recovery.

Application to fruit import/export data illustrates practical utility.

Abstract

Multidimensional network data can have different levels of complexity, as nodes may be characterized by heterogeneous individual-specific features, which may vary across the networks. This paper introduces a class of models for multidimensional network data, where different levels of heterogeneity within and between networks can be considered. The proposed framework is developed in the family of latent space models, and it aims to distinguish symmetric relations between the nodes and node-specific features. Model parameters are estimated via a Markov Chain Monte Carlo algorithm. Simulated data and an application to a real example, on fruits import/export data, are used to illustrate and comment on the performance of the proposed models.