Abusing a hypergraph partitioner for unweighted graph partitioning

We investigate using the Mondriaan matrix partitioner for unweighted graph partitioning in the communication volume and edge-cut metrics. By converting the unweighted graphs to appropriate matrices, we measure Mondriaan’s performance as a graph partitioner for the 10th DIMACS challenge on graph partitioning and clustering. We find that Mondriaan can effectively be used as a graph partitioner: w.r.t. the edge-cut metric, Mondriaan’s best results are on average within 13% of the best known results as listed in Chris Walshaw’s partitioning archive, but it is an order of magnitude slower than dedicated graph partitioners.