A study of graph partitioning schemes for parallel graph community detection

This paper presents a study of graph partitioning schemes for parallel graph community detection on distributed memory machines. We investigate the relationship between graph structure and parallel clustering effectiveness, and develop a heuristic partitioning algorithm suitable for modularity-based algorithms. We demonstrate the accuracy and scalability of our approach using several real-world large graph datasets compared with state-of-the-art parallel algorithms on the Cray XK7 supercomputer at Oak Ridge National Laboratory. Given the ubiquitous graph model, we expect this high-performance solution will help lead to new insights in numerous fields.