Stratified Stochastic Variational Inference for High-Dimensional Network Factor Model
针对高维网络数据的潜因子模型，提出分层随机变分推断算法，利用网络数据的稀疏性大幅降低计算和存储成本，适用于数千节点的场景，并提供了R包。
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. An R package with an efficient c++ implementation of the proposed algorithms is provided.