Stochastic Block Transition Models for Dynamic Networks
This addresses the need for more accurate dynamic network models in social network analysis, though it is incremental as it builds on existing stochastic block model extensions.
The paper tackles the problem of modeling dynamic networks by proposing a stochastic block transition model (SBTM) that allows edge presence to directly influence future probabilities without a hidden Markov assumption, and demonstrates it significantly improves reproduction of edge durations in real social network data.
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.