Scaling Up Subgraph Query Processing with Efficient Subgraph Matching

Abstract

A subgraph query finds all data graphs in a graph database each of which contains the given query graph. Existing work takes the indexing-filtering-verification (IFV) approach to first index all data graphs, then filter out some of them based on the index, and finally test subgraph isomorphism on each of the remaining data graphs. This final test of subgraph isomorphism is a sub-problem of subgraph matching, which finds all subgraph isomorphisms from a query graph to a data graph. As such, in this paper, we study whether, and if so, how to utilize efficient subgraph matching to improve subgraph query processing. Specifically, we modify leading subgraph matching algorithms and integrate them with top-performing subgraph querying algorithms. Our results show that (1) the slow verification method in existing IFV algorithms can lead us to over-estimate the gain of filtering; and (2) our modified subgraph querying algorithms with efficient subgraph matching are competitive in time performance and can scale to hundreds of thousands of data graphs and graphs of thousands of vertices.