  In this work we propose a simple and easily parallelizable algorithm for
multiway graph partitioning. The algorithm alternates between three basic
components: diffusing seed vertices over the graph, thresholding the diffused
seeds, and then randomly reseeding the thresholded clusters. We demonstrate
experimentally that the proper combination of these ingredients leads to an
algorithm that achieves state-of-the-art performance in terms of cluster purity
on standard benchmarks datasets. Moreover, the algorithm runs an order of
magnitude faster than the other algorithms that achieve comparable results in
terms of accuracy. We also describe a coarsen, cluster and refine approach
similar to GRACLUS and METIS that removes an additional order of magnitude from
the runtime of our algorithm while still maintaining competitive accuracy.
