  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.
