Abstract. We outline a method for constructing in parallel a collection of local clusters for a massive distributed graph. For a given input set of (vertex, cluster size) tuples, we compute approximations of personal PageRank vectors in parallel using Pregel, and sweep the results using MapReduce. We show our method converges to the serial approximate PageRank, and perform an experiment that illustrates the speed up over the serial method. We also outline a random selection and deconfliction procedure to cluster a distributed graph, and perform experiments to determine the quality of clusterings returned.