10X Faster Subgraph Matching: Dual Matching Networks with Interleaved Diffusion Attention

The goal of subgraph matching is to determine the presence of a particular query pattern within a large collection of data graphs. Despite being a hard problem, subgraph matching is essential in various disciplines, including bioinformatics, text matching, and graph retrieval. Although traditional approaches could provide exact solutions, their computations are known to be NP-complete, leading to an overwhelmingly querying latency. While recent neural-based approaches have been shown to improve the response time, the oversimplified assumption of the first-order network may neglect the generalisability of fully capturing patterns in varying sizes, causing the performance to drop significantly in datasets in various domains. To overcome these limitations, this paper proposes xDualSM, a dual matching neural network model with interleaved diffusion attention. Specifically, we first embed the structural information of graphs into different adjacency matrices, which explicitly capture the intra-graph and cross-graph structures between the query pattern and the target graph. Then, we introduce a dual matching network with interleaved diffusion attention to carefully capture intra-graph and cross-graph information while reducing computational complexity. Empirically, our proposed framework not only boosted the speed of subgraph matching more than 10x compared to the fastest baseline but also achieved significant improvements of 47.64% in Recall and 34.39% in F1-score compared to the state-of-the-art approximation approach on COX2 dataset. In addition, our results are comparable with exact methods.