CEMR: An Effective Subgraph Matching Algorithm with Redundant Extension Elimination

TL;DR

CEMR is a new subgraph matching algorithm that reduces duplicate computations and prunes unpromising search paths, significantly improving efficiency on large graphs.

Contribution

The paper introduces CEMR, a novel subgraph matching algorithm with techniques for extension merging, reusing, and pruning, addressing duplicate computation issues in existing methods.

Findings

CEMR outperforms existing methods on real-world datasets.

It reduces runtime by minimizing duplicate extension computations.

Experimental results validate its effectiveness across diverse workloads.

Abstract

Subgraph matching is a fundamental problem in graph analysis with a wide range of applications. However, due to its inherent NP-hardness, enumerating subgraph matches efficiently on large real-world graphs remains highly challenging. Most existing works adopt a depth-first search (DFS) backtracking strategy, where a partial embedding is gradually extended in a DFS manner along a branch of the search trees until either a full embedding is found or no further extension is possible. A major limitation of this paradigm is the significant amount of duplicate computation that occurs during enumeration, which increases the overall runtime. To overcome this limitation, we propose a novel subgraph matching algorithm, CEMR. It incorporates two techniques to reduce duplicate extensions: common extension merging, which leverages a black-white vertex encoding, and common extension reusing, which employs common extension buffers. In addition, we design two pruning techniques to discard unpromising search branches. Extensive experiments on real-world datasets and diverse query workloads demonstrate that CEMR outperforms state-of-the-art subgraph matching methods.