Stochastic Block Transition Models for Dynamic Networks

TL;DR

This paper introduces a stochastic block transition model (SBTM) for dynamic networks that captures edge dynamics without hidden Markov assumptions, improving the modeling of real social network data.

Contribution

The paper proposes the SBTM, a novel dynamic network model that directly links edge presence to future probabilities without hidden Markov assumptions.

Findings

SBTM better reproduces edge durations in social networks.

The inference procedure is computationally efficient.

Model outperforms existing dynamic network models.

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.