Joint Modelling of Multiple Network Views

TL;DR

This paper introduces a variational Bayes method for latent space models and extends it to jointly analyze multiple network views, demonstrated on social and biological datasets.

Contribution

It proposes the latent space joint model (LSJM) for integrating multiple network views using a shared latent space, advancing network data analysis.

Findings

Effective joint modeling of multiple networks demonstrated on real datasets

Improved understanding of node relationships across different network types

Method provides a scalable approach for complex network analysis

Abstract

Latent space models (LSM) for network data were introduced by Hoff et al. (2002) under the basic assumption that each node of the network has an unknown position in a D-dimensional Euclidean latent space: generally the smaller the distance between two nodes in the latent space, the greater their probability of being connected. In this paper we propose a variational Bayes approach to estimate the intractable posterior of the LSM. In many cases, different network views on the same set of nodes are available. It can therefore be useful to build a model able to jointly summarise the information given by all the network views. For this purpose, we introduce the latent space joint model (LSJM) that merges the information given by multiple network views assuming that the probability of a node being connected with other nodes in each network view is explained by a unique latent variable. This model is demonstrated on the analysis of two datasets: the excerpt of 50 girls from `Teenage Friends and Lifestyle Study' data at three time points and the Saccharomyces cerevisiae genetic and physical protein-protein interactions.