Towards a Scalable Parallel Infomap Algorithm for Community Detection

Identifying Community structures is a fundamental problem in graph analysis. To detect communities in massive contemporary graphs, researchers have extensively explored shared- and distributed-memory parallel algorithms for several methods including Louvain Modularity Optimization and Label Propagation. The widely used Infomap algorithm based on Map Equation Framework (MEF) is known to provide better quality results than other approaches. However, research on parallel community detection using MEF or Infomap is extremely sparse when compared to other methods. We present a comprehensive characterization of Infomap and some of its known parallel implementations to facilitate research into parallel algorithms based on MEF. Most implementations take simple parallelization approaches, leaving strategies used to parallelize similar algorithms such as Louvain untouched. We highlight the scalability limitations of current implementations and implement and eval-uate optimizations for MEF based parallel community detection that achieved up to 119% improvement on the overall speedup across the tested datasets.