Stratified stochastic variational inference for high-dimensional network factor model

TL;DR

This paper introduces scalable stochastic variational inference algorithms for high-dimensional network latent factor models, enabling efficient Bayesian analysis of large networks with thousands of nodes.

Contribution

It develops novel stochastic optimization algorithms that leverage sparse data representations for scalable Bayesian inference in high-dimensional network models.

Findings

Algorithms significantly reduce computational time.

Able to handle networks with thousands of nodes.

Demonstrates efficiency and scalability in large network settings.

Abstract

There has been considerable recent interest in Bayesian modeling of high-dimensional networks via latent space approaches. When the number of nodes increases, estimation based on Markov Chain Monte Carlo can be extremely slow and show poor mixing, thereby motivating research on alternative algorithms that scale well in high-dimensional settings. In this article, we focus on the latent factor model, a widely used approach for latent space modeling of network data. We develop scalable algorithms to conduct approximate Bayesian inference via stochastic optimization. Leveraging sparse representations of network data, the proposed algorithms show massive computational and storage benefits, and allow to conduct inference in settings with thousands of nodes.