GScan: Exploiting Sequential Scans for Subgraph Matching

Abstract

Subgraph matching is to enumerate all the subgraphs of a graph that is isomorphic to the query graph. It is a critical component of many applications such as clustering coefficient computation and trend evolution. As the real-world graph grows explosively, we have massive graphs that are much larger than the memory size of the modern machines. Therefore, in this paper, we study the subgraph matching problem where the graph is stored on disk. Different from the existing approaches, we design a block-based approach, $\mathsf {GScan}$, which investigates the schedule of the blocks transferred between the memory and the disk. To achieve high I/O efficiency, $\mathsf {GScan}$ only uses sequential I/O read operations. We conduct experimental studies to demonstrate the efficiency of our block-based approach.