On the Parallelization of MCMC for Community Detection

The rapid growth in size of real-world graph datasets necessitates the design of parallel and scalable graph analytics algorithms for large graphs. Community detection is a graph analysis technique with use cases in many domains from bioinformatics to network security. Markov chain Monte Carlo (MCMC)-based methods for performing community detection, such as the stochastic block partitioning (SBP) algorithm, are robust to graphs with a complex structure, but have traditionally been difficult to parallelize due to the serial nature of the underlying MCMC algorithm. This paper presents hybrid SBP (H-SBP), a novel hybrid method to parallelize the inherently sequential computation within each MCMC chain, for SBP. H-SBP processes a fraction of the most influential graph vertices serially and the remaining majority of the vertices in parallel using asynchronous Gibbs. We empirically show that H-SBP speeds up the MCMC computations by up to 5.6 × on real-world graphs while maintaining accuracy.