Stochastic Block Transition Models for Dynamic Networks

Kevin S. Xu

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arXiv · 1411.5404

Stochastic Block Transition Models for Dynamic Networks

Kevin S. Xu

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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.

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cs.SI cs.LG physics.soc-ph stat.ME

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BibTeX RIS

Kevin S. Xu. 2014-11-19. Stochastic Block Transition Models for Dynamic Networks. https://arxiv.org/abs/1411.5404

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