xNeuSM: Explainable Neural Subgraph Matching with Graph Learnable Multi-hop Attention Networks

• URL: http://arxiv.org/abs/2312.01612v1
• Date: Mon, 4 Dec 2023 04:03:30 GMT
• Title: xNeuSM: Explainable Neural Subgraph Matching with Graph Learnable Multi-hop Attention Networks
• Authors: Duc Q. Nguyen, Thanh Toan Nguyen, Tho quan
• Abstract summary: Subgraph matching is a challenging problem with a wide range of applications in database systems, biochemistry, and cognitive science. Traditional graph-matching algorithms provide precise results but face challenges in large graph instances due to the NP-complete problem. This article introduces Graph Learnable Multi-hop Attention Networks (GLeMA) that adaptively learns the parameters governing the attention factor decay for each node across hops.
• Score: 2.084955943646144
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Subgraph matching is a challenging problem with a wide range of applications in database systems, biochemistry, and cognitive science. It involves determining whether a given query graph is present within a larger target graph. Traditional graph-matching algorithms provide precise results but face challenges in large graph instances due to the NP-complete problem, limiting their practical applicability. In contrast, recent neural network-based approximations offer more scalable solutions, but often lack interpretable node correspondences. To address these limitations, this article presents xNeuSM: Explainable Neural Subgraph Matching which introduces Graph Learnable Multi-hop Attention Networks (GLeMA) that adaptively learns the parameters governing the attention factor decay for each node across hops rather than relying on fixed hyperparameters. We provide a theoretical analysis establishing error bounds for GLeMA's approximation of multi-hop attention as a function of the number of hops. Additionally, we prove that learning distinct attention decay factors for each node leads to a correct approximation of multi-hop attention. Empirical evaluation on real-world datasets shows that xNeuSM achieves substantial improvements in prediction accuracy of up to 34% compared to approximate baselines and, notably, at least a seven-fold faster query time than exact algorithms. The source code of our implementation is available at https://github.com/martinakaduc/xNeuSM.

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