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Graph partition is a key component to achieve workload balance and reduce jobcompletion time in parallel graph processing systems. Among the variouspartition strategies, edge partition has demonstrated more promisingperformance in power-law graphs than vertex partition and thereby has been morewidely adopted as the default partition strategy by existing graph systems. Thegraph edge partition problem, which is to split the edge set into multiplebalanced parts to minimize the total number of copied vertices, has been widelystudied from the view of optimization and algorithms. In this paper, we studylocal search algorithms for this problem to further improve the partitionresults from existing methods. More specifically, we propose two novelconcepts, namely adjustable edges and blocks. Based on these, we develop agreedy heuristic as well as an improved search algorithm utilizing the propertyof the max-flow model. To evaluate the performance of our algorithms, we firstprovide adequate theoretical analysis in terms of the approximation quality. Wesignificantly improve the previously known approximation ratio for thisproblem. Then we conduct extensive experiments on a large number of benchmarkdatasets and state-of-the-art edge partition strategies. The results show thatour proposed local search framework can further improve the quality of graphpartition by a wide margin.

Enhancing Balanced Graph Edge Partition with Effective Local Search