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Graph partition is a fundamental problem of parallel computing for big graphdata. Many graph partition algorithms have been proposed to solve the problemin various applications, such as matrix computations and PageRank, etc., butnone has pay attention to random walks. Random walks is a widely used method toexplore graph structure in lots of fields. The challenges of graph partitionfor random walks include the large number of times of communication betweenpartitions, lots of replications of the vertices, unbalanced partition, etc. Inthis paper, we propose a feasible graph partition framework for random walksimplemented by parallel computing in big graph. The framework is based on twooptimization functions to reduce the bandwidth, memory and storage cost in thecondition that the load balance is guaranteed. In this framework, severalgreedy graph partition algorithms are proposed. We also propose five metricsfrom different perspectives to evaluate the performance of these algorithms. Byrunning the algorithms on the big graph data set of real world, theexperimental results show that these algorithms in the framework are capable ofsolving the problem of graph partition for random walks for different needs,e.g. the best result is improved more than 70 times in reducing the times ofcommunication.

A Feasible Graph Partition Framework for Random Walks Implemented by Parallel Computing in Big Graph