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GraphA: Adaptive Partitioning for Natural Graphs
Dongsheng Li, Chengfei Zhang, Jinyan Wang, Zhaoning Zhang and Yiming Zhang
National University of Defense Technology, National University of Defense Technology, National University of Defense Technology, National University of Defense Technology, National University of Defense Technology

Large-scale graph computation is central to applications ranging from language processing to social networks. However, natural graphs tend to have skewed power-law distributions where a small subset of the vertices have a large number of neighbors. Existing graph-parallel systems suffer from load imbalance, high communication cost, or suboptimal and complex processing. In this paper we present GraphA, an Adaptive approach to efficient partitioning and computation of large-scale natural graphs. GraphA provides an adaptive and uniform graph partitioning algorithm, which partitions the datasets in a load-balanced manner by using an incremental number of hash functions. We have implemented GraphA both on Spark and on GraphLab. Extensive evaluation shows that GraphA remarkably outperforms state-of-the-art graph-parallel systems (GraphX and PowerLyra) in ingress time, execution time and storage overhead, for both real-world and synthetic graphs.