Edge Property Based Stream Order Reduce the Performance of Stream Edge Graph Partition

Abstract

The graph data is found everywhere in various disciplines, such as in social networks, biological networks, chemical compounds, and computer vision, etc. Currently, the size of graph data has dramatically increased; all disciplines have extracted knowledge from a graph by partitioning and distributing large-scale graph into different clusters using the distributed graph processing system or graph database. However, the graph partitioning has impacted to speed up or slow down the performance of those systems. Even if the stream edge graph partition has shown better partition quality than vertex graph partition for skew degree distribution of a graph and supports big graph partitioning, stream graph edge partitioners are affected by stream orders. In this study, we propose two edge properties based stream order models, TFB(Tree edges First, then Backward edges follow) and BFT(Backward edges First, then Tree edges follow). And study the effect of stream order on stream graph edge partitioners. The results show that TFB and BFT models significantly affect the quality of stream edge partition, except hashing. All algorithms show the best partitioning quality by Random order than other orders, TFB, BFT, BFS(Breadth-First Search), and DFS(Depth-First Search).