Sub-GMN: The Neural Subgraph Matching Network Model

TL;DR

Sub-GMN is an innovative neural network model that improves subgraph matching accuracy and speed, allowing flexible query and data graphs, and providing explicit node-to-node match outputs.

Contribution

This paper introduces Sub-GMN, a novel end-to-end learning-based approach that enhances subgraph matching by combining graph embeddings, metric learning, and attention, with improved flexibility and output interpretability.

Findings

Sub-GMN achieves higher accuracy than GNN and FGNN baselines.

Sub-GMN runs 20-40 times faster than FGNN.

Sub-GMN attains an F1-score of 0.95 on dataset 2.

Abstract

As one of the most fundamental tasks in graph theory, subgraph matching is a crucial task in many fields, ranging from information retrieval, computer vision, biology, chemistry and natural language processing. Yet subgraph matching problem remains to be an NP-complete problem. This study proposes an end-to-end learning-based approximate method for subgraph matching task, called subgraph matching network (Sub-GMN). The proposed Sub-GMN firstly uses graph representation learning to map nodes to node-level embedding. It then combines metric learning and attention mechanisms to model the relationship between matched nodes in the data graph and query graph. To test the performance of the proposed method, we applied our method on two databases. We used two existing methods, GNN and FGNN as baseline for comparison. Our experiment shows that, on dataset 1, on average the accuracy of Sub-GMN are 12.21\% and 3.2\% higher than that of GNN and FGNN respectively. On average running time Sub-GMN runs 20-40 times faster than FGNN. In addition, the average F1-score of Sub-GMN on all experiments with dataset 2 reached 0.95, which demonstrates that Sub-GMN outputs more correct node-to-node matches. Comparing with the previous GNNs-based methods for subgraph matching task, our proposed Sub-GMN allows varying query and data graphes in the test/application stage, while most previous GNNs-based methods can only find a matched subgraph in the data graph during the test/application for the same query graph used in the training stage. Another advantage of our proposed Sub-GMN is that it can output a list of node-to-node matches, while most existing end-to-end GNNs based methods cannot provide the matched node pairs.