Scaling up Dynamic Edge Partition Models via Stochastic Gradient MCMC
Sikun Yang Heinz Koeppl
The edge partition model (EPM) is a generative model for extracting an
overlapping community structure from static graph-structured data. In the EPM,
the gamma process (GaP) prior is adopted to infer the appropriate number of
latent communities, and each vertex is endowed with a gamma distributed
positive memberships vector. Despite having many attractive properties,
inference in the EPM is typically performed using Markov chain Monte Carlo
(MCMC) methods that prevent it from being applied to massive network data. In
this paper, we generalize the EPM to account for dynamic enviroment by
representing each vertex with a positive memberships vector constructed using
Dirichlet prior specification, and capturing the time-evolving behaviour of
vertices via a Dirichlet Markov chain construction. A simple-to-implement Gibbs
sampler is proposed to perform posterior computation using Negative- Binomial
augmentation technique. For large network data, we propose a stochastic
gradient Markov chain Monte Carlo (SG-MCMC) algorithm for scalable inference in
the proposed model. The experimental results show that the novel methods
achieve competitive performance in terms of link prediction, while being much
faster.