OptMatch: An Efficient and Generic Neural Network-Assisted Subgraph Matching Approach

The graph has been widely used to model the entities and the relationships among them in real-world applications. Subgraph matching is a core operation in graph data analysis. However, existing exact matching methods may incur high cost as their searched branches are always unpromising. In recent years, several approximate matching solutions have been proposed by exploiting neural networks. Nevertheless, the accuracy of the returned approximate results could be improved significantly. Motivated by these observations, we proposed OptMatch, an efficient and generic neural network-assisted subgraph matching approach, in this work. In particular, OptMatch proposes a novel subgraph partial embedding network and implements carefully designed search strategies to optimize search processes during the subgraph matching process. First, it can be used to accelerate existing exact matching methods. Moreover, it is also an approximate matching solution, which offers better accuracy compared to existing approximate solutions. We conduct exten-sive experiments on seven real-world data graphs to demonstrate the superiority of OptMatch in both exact and approximate subgraph matching.