论文标题
高维网络因子模型的分层随机变异推断
Stratified stochastic variational inference for high-dimensional network factor model
论文作者
论文摘要
最近，通过潜在空间方法对高维网络的贝叶斯建模引起了极大的兴趣。当节点的数量增加时，基于马尔可夫链蒙特卡洛的估计可能非常慢，并且表现出较差的混合，从而激发了对在高维环境中进行良好扩展的替代算法的研究。在本文中，我们专注于潜在因子模型，这是一种广泛使用的网络数据潜在空间建模的方法。我们开发可扩展的算法通过随机优化进行近似贝叶斯推断。利用网络数据的稀疏表示，提出的算法显示出巨大的计算和存储益处，并允许在具有数千个节点的设置中进行推断。
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.
