ENDNet: Extra-Node Decision Network for Subgraph Matching

Subgraph matching, which involves identifying a query graph within a larger data graph, is a fundamental problem that has applications in various fields. Although graph neural networks (GNNs) are commonly used in learning-based subgraph matching, they face challenges due to their convolution process. Specifically, GNNs generate new node features by aggregating information from adjacent nodes, meaning that even if nodes in the query and data graphs start with identical features, the presence of extraneous nodes or edges in the data graph can distort feature representations and hinder accurate matching. Ensuring feature similarity between the query and data graphs despite these extraneous elements is a key challenge. To address this, we propose ENDNet, a model that improves subgraph matching by detecting and mitigating the influence of extraneous nodes. ENDNet uses a denormalized matching matrix to identify extra nodes and neutralizes their impact by setting their feature values to zero before aggregation and convolution. Additionally, ENDNet incorporates a shared-graph convolutional network, leveraging the sigmoid function to refine feature extraction. Experiments on four open datasets demonstrate that ENDNet outperforms existing trainable subgraph matching models, improving accuracy from 91.6% to 99.1% on the COX2 dataset. Ablation studies further confirm the effectiveness of extra-node determination and the shared-graph convolutional network, highlighting their importance in subgraph matching. The code for ENDNet is available on GitHub.