Stratified stochastic variational inference for high-dimensional network factor model

  title={Stratified stochastic variational inference for high-dimensional network factor model},
  author={Emanule Aliverti and Massimiliano Russo},
  journal={Journal of Computational and Graphical Statistics},
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… Expand

Figures and Tables from this paper

A Latent Space Model for Multilayer Network Data
A Bayesian statistical model to simultaneously characterize two or more social networks defined over a common set of actors with a hierarchical prior distribution is proposed, achieving a compromise between dependent and independent networks. Expand
An Eigenmodel for Dynamic Multilayer Networks
Dynamic multilayer networks frequently represent the structure of multiple co-evolving relations; however, statistical models are not well-developed for this prevalent network type. Here, we proposeExpand


Fast Inference for the Latent Space Network Model Using a Case-Control Approximate Likelihood
  • A. Raftery, X. Niu, Peter D. Hoff, K. Y. Yeung
  • Mathematics, Medicine
  • Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America
  • 2012
A case-control log-likelihood is constructed, which is an unbiased estimator of the log-full likelihood of the latent space model, and is fitted to a large protein–protein interaction data using the case- control likelihood and used to identify false positive links. Expand
Stochastic variational inference
Stochastic variational inference lets us apply complex Bayesian models to massive data sets, and it is shown that the Bayesian nonparametric topic model outperforms its parametric counterpart. Expand
Variational Bayesian inference for the Latent Position Cluster Model for network data
Analyzing Networks and Learning with Graphs Workshop at 23rd annual conference on Neural Information Processing Systems (NIPS 2009), Whister, December 11 2009
A Stochastic Approximation Method
Let M(x) denote the expected value at level x of the response to a certain experiment. M(x) is assumed to be a monotone function of x but is unknown tot he experiment, and it is desire to find theExpand
Conditionally Conjugate Mean-Field Variational Bayes for Logistic Models
Variational Bayes (VB) is a common strategy for approximate Bayesian inference, but simple methods are only available for specific classes of models including, in particular, representations havingExpand
Variational Inference: A Review for Statisticians
Variational inference (VI), a method from machine learning that approximates probability densities through optimization, is reviewed and a variant that uses stochastic optimization to scale up to massive data is derived. Expand
Automatic Variational Inference in Stan
An automatic variational inference algorithm, automatic differentiation Variational inference (ADVI), which is implemented in Stan, a probabilistic programming system and can be used on any model the authors write in Stan. Expand
Pattern Recognition and Machine Learning
Probability Distributions, linear models for Regression, Linear Models for Classification, Neural Networks, Graphical Models, Mixture Models and EM, Sampling Methods, Continuous Latent Variables, Sequential Data are studied. Expand
Multi-scale Attributed Node Embedding
It is proved theoretically that matrices of node-feature pointwise mutual information are implicitly factorized by the embeddings, and computationally efficient and outperform comparable models on social networks and web graphs. Expand
Bayesian Analysis, 12(2):351–377
  • Stan Development Team
  • 2019