SUFF: Accelerating Subgraph Matching with Historical Data
Xun Jian, Zhiyuan Li, Lei Chen
Abstract
Subgraph matching is a fundamental problem in graph theory and has wide applications in areas like sociology, chemistry, and social networks. Due to its NP-hardness, the basic approach is a brute-force search over the whole search space. Some pruning strategies have been proposed to reduce the search space. However, they are either space-inefficient or based on assumptions that the graph has specific properties. In this paper, we propose SUFF, a general and powerful structure filtering framework, which can accelerate most of the existing approaches with slight modifications. Specifically, it builds a set of filters using matching results of past queries, and uses them to prune the search space for future queries. By fully utilizing the relationship between matches of two queries, it ensures that such pruning is sound. Furthermore, several optimizations are proposed to reduce the computation and space cost for building, storing, and using filters. Extensive experiments are conducted on multiple real-world data sets and representative existing approaches. The results show that SUFF can achieve up to 15X speedup with small overheads.