Enhancing Balanced Graph Edge Partition with Effective Local Search

TL;DR

This paper introduces novel local search algorithms for edge partitioning in graphs, significantly improving partition quality and approximation ratios, especially for power-law graphs, through theoretical analysis and extensive experiments.

Contribution

It proposes two new concepts, adjustable edges and blocks, and develops algorithms that enhance existing edge partitioning methods with better approximation guarantees.

Findings

Improved approximation ratio for edge partitioning problem.

Significant enhancement in partition quality on benchmark datasets.

Effective local search framework outperforming state-of-the-art strategies.

Abstract

Graph partition is a key component to achieve workload balance and reduce job completion time in parallel graph processing systems. Among the various partition strategies, edge partition has demonstrated more promising performance in power-law graphs than vertex partition and thereby has been more widely adopted as the default partition strategy by existing graph systems. The graph edge partition problem, which is to split the edge set into multiple balanced parts to minimize the total number of copied vertices, has been widely studied from the view of optimization and algorithms. In this paper, we study local search algorithms for this problem to further improve the partition results from existing methods. More specifically, we propose two novel concepts, namely adjustable edges and blocks. Based on these, we develop a greedy heuristic as well as an improved search algorithm utilizing the property of the max-flow model. To evaluate the performance of our algorithms, we first provide adequate theoretical analysis in terms of the approximation quality. We significantly improve the previously known approximation ratio for this problem. Then we conduct extensive experiments on a large number of benchmark datasets and state-of-the-art edge partition strategies. The results show that our proposed local search framework can further improve the quality of graph partition by a wide margin.