Graph Partitioning for Distributed Graph Processing

There is a large demand for distributed engines that efficiently process large-scale graph data, such as social graph and web graph. The distributed graph engines execute analysis process after partitioning input graph data and assign them to distributed computers, so the quality of graph partitioning largely affects the communication cost and load balance among computers during the analysis process. We propose an effective graph partitioning technique that achieves low communication cost and good load balance among computers at the same time. We first generate more clusters than the number of computers by extending the modularity-based clustering, and then merge those clusters into balanced-size clusters until the number of clusters becomes the number of computers by using techniques designed for graph packing problem. We implemented our technique on top of distributed graph engine, PowerGraph, and made intensive experiments. The results show that our partitioning technique reduces the communication cost so it improves the response time of graph analysis patterns. In particular, PageRank computation is 3.2 times faster at most than HDRF, the state-of-the art of streaming-based partitioning approach. © 2017, The Author(s).