OffStreamNG: Partial Stream Hybrid Graph Edge Partitioning Based on Neighborhood Expansion and Greedy Heuristic

Abstract

Recently, graph edge partitioning has shown better partitioning quality than the vertex graph partitioning for the skewed degree distribution of real-world graph data. Graph edge partitioning can be classified as stream and offline. The stream edge partitioning approach supports a big graph partitioning; however, it has lower partitioning quality, is affected by stream order, and it has taken much time to make partitioning compared with the offline edge partitioning. Conversely, the offline edge partitioning approach has better partitioning quality than stream edge partitioning; however, it does not support big graph partitioning. In this study, we propose partial stream hybrid graph edge partitioning OffStreamNG, which leverages the advantage of both offline and stream edge partitioning approaches by interconnecting via saved partition state layer. The OffStreamNG holds vertex and load states as partition state, while the offline component is partitioning using neighborhood expansion heuristic. And it is transferring this partition state to the online component of Greedy heuristic with minor modification of both algorithms. Experimental results show that OffStreamNG achieves attractive results in terms of replication factor, load balance, and total partitioning time.