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.
Parallel Comput.