Accelerating the Information-Theoretic Approach of Community Detection Using Distributed and Hybrid Memory Parallel Schemes

There are several approaches for discovering communities in a network (graph). Despite being approximating in nature, discovering communities based on the laws of Information Theory has a proven standard of accuracy. The information-theoretic algorithm known as Infomap developed a decade ago for detecting communities, did not foresee the tremendous growth of social networking, multimedia, and massive information boom. To discover communities in massive networks, we have designed a distributed-memory-parallel Infomap in the MPI framework. Our design reaches scalability of over 500 processes capable of processing networks with millions of edges while maintaining quality comparable to the sequential Infomap. We have further developed a novel parallel hybrid approach for Infomap consists of both distributed and shared memory parallelism using MPI and OpenMP frameworks. This achieves a speedup of more than 11x in processing a network of over 100 million edges which is significantly greater than the state-of-the-art techniques