Detecting Small Query Graphs in A Large Graph via Neural Subgraph Search

TL;DR

This paper introduces NSUBS, a neural network-based approach for efficiently detecting small query graphs within large target graphs, addressing the challenges of subgraph matching with innovative architecture and training methods.

Contribution

The paper proposes a novel neural network architecture and look-ahead loss function to improve subgraph matching performance in large graphs using reinforcement learning.

Findings

Significantly improved subgraph matching accuracy on real-world graphs.

Effective neural architecture for dynamic matching information computation.

Enhanced training method with look-ahead loss for better policy learning.

Abstract

Recent advances have shown the success of using reinforcement learning and search to solve NP-hard graph-related tasks, such as Traveling Salesman Optimization, Graph Edit Distance computation, etc. However, it remains unclear how one can efficiently and accurately detect the occurrences of a small query graph in a large target graph, which is a core operation in graph database search, biomedical analysis, social group finding, etc. This task is called Subgraph Matching which essentially performs subgraph isomorphism check between a query graph and a large target graph. One promising approach to this classical problem is the "learning-to-search" paradigm, where a reinforcement learning (RL) agent is designed with a learned policy to guide a search algorithm to quickly find the solution without any solved instances for supervision. However, for the specific task of Subgraph Matching, though the query graph is usually small given by the user as input, the target graph is often orders-of-magnitude larger. It poses challenges to the neural network design and can lead to solution and reward sparsity. In this paper, we propose NSUBS with two innovations to tackle the challenges: (1) A novel encoder-decoder neural network architecture to dynamically compute the matching information between the query and the target graphs at each search state; (2) A novel look-ahead loss function for training the policy network. Experiments on six large real-world target graphs show that NSUBS can significantly improve the subgraph matching performance.