Fast Subgraph Matching by Exploiting Search Failures

TL;DR

This paper introduces a novel subgraph matching algorithm that learns from failures during backtracking to prune search space effectively, significantly enhancing performance on complex graphs.

Contribution

It proposes an on-the-fly pruning technique using failure pattern learning, which improves subgraph matching efficiency without affecting correctness.

Findings

Performance improved by up to 10,000 times

Effective pruning reduces search space significantly

Applicable to complex graph structures

Abstract

Subgraph matching is a compute-intensive problem that asks to enumerate all the isomorphic embeddings of a query graph within a data graph. This problem is generally solved with backtracking, which recursively evolves every possible partial embedding until it becomes an isomorphic embedding or is found unable to become it. While existing methods reduce the search space by analyzing graph structures before starting the backtracking, it is often ineffective for complex graphs. In this paper, we propose an efficient algorithm for subgraph matching that performs on-the-fly pruning during the backtracking. Our main idea is to `learn from failure'. That is, our algorithm generates failure patterns when a partial embedding is found unable to become an isomorphic embedding. Then, in the subsequent process of the backtracking, our algorithm prunes partial embeddings matched with a failure pattern. This pruning does not change the result because failure patterns are designed to represent the conditions that never yield an isomorphic embedding. Additionally, we introduce an efficient representation of failure patterns for constant-time pattern matching. The experimental results show that our method improves the performance by up to 10000 times than existing methods.