Variational Inference for Latent Space Models for Dynamic Networks

TL;DR

This paper introduces a variational inference method for latent space models in dynamic networks, enabling faster analysis of large networks with theoretical guarantees and practical demonstrations.

Contribution

It presents a novel variational approach for latent space models that is computationally efficient and scalable to large dynamic networks, with theoretical analysis included.

Findings

Faster inference compared to MCMC methods

Effective on both simulated and real data

Theoretical properties of the variational Bayes risk established

Abstract

Latent space models are popular for analyzing dynamic network data. We propose a variational approach to estimate the model parameters as well as the latent positions of the nodes in the network. The variational approach is much faster than Markov chain Monte Carlo algorithms, and is able to handle large networks. Theoretical properties of the variational Bayes risk of the proposed procedure are provided. We apply the variational method and latent space model to simulated data as well as real data to demonstrate its performance.