A Latent Shrinkage Position Model for Binary and Count Network Data

TL;DR

The paper introduces a Bayesian nonparametric latent position model that automatically infers the effective dimension of the latent space in network data, simplifying analysis of binary and count networks.

Contribution

It proposes the LSPM with a shrinkage prior that determines the latent space dimension without requiring model selection or multiple fits.

Findings

LSPM accurately infers latent space dimensions in simulations.

The model effectively analyzes real binary and count network data.

Computational efficiency is improved with novel surrogate proposal distributions.

Abstract

Interactions between actors are frequently represented using a network. The latent position model is widely used for analysing network data, whereby each actor is positioned in a latent space. Inferring the dimension of this space is challenging. Often, for simplicity, two dimensions are used or model selection criteria are employed to select the dimension, but this requires choosing a criterion and the computational expense of fitting multiple models. Here the latent shrinkage position model (LSPM) is proposed which intrinsically infers the effective dimension of the latent space. The LSPM employs a Bayesian nonparametric multiplicative truncated gamma process prior that ensures shrinkage of the variance of the latent positions across higher dimensions. Dimensions with non-negligible variance are deemed most useful to describe the observed network, inducing automatic inference on the latent space dimension. While the LSPM is applicable to many network types, logistic and Poisson LSPMs are developed here for binary and count networks respectively. Inference proceeds via a Markov chain Monte Carlo algorithm, where novel surrogate proposal distributions reduce the computational burden. The LSPM's properties are assessed through simulation studies, and its utility is illustrated through application to real network datasets. Open source software assists wider implementation of the LSPM.