Stochastic Block Transition Models for Dynamic Networks

Abstract

There has been great interest in recent years on statistical models for dynamic networks. In this paper, I propose a stochastic block transition model (SBTM) for dynamic networks that is inspired by the well-known stochastic block model (SBM) for static networks and previous dynamic extensions of the SBM. Unlike most existing dynamic network models, it does not make a hidden Markov assumption on the edge-level dynamics, allowing the presence or absence of edges to directly influence future edge probabilities while retaining the interpretability of the SBM. I derive an approximate inference procedure for the SBTM and demonstrate that it is significantly better at reproducing durations of edges in real social network data.

To appear in proceedings of AISTATS 2015

References in corpus (2)

• Stochastic blockmodels and community structure in networks
• Nonparametric Bayes dynamic modeling of relational data

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• Variational Inference for Latent Space Models for Dynamic Networks
• Consistency of the maximum likelihood and variational estimators in a dynamic stochastic block model