Reinforcement Learning Based Query Vertex Ordering Model for Subgraph Matching

TL;DR

This paper introduces a reinforcement learning and graph neural network-based model to optimize query vertex ordering in subgraph matching, significantly improving efficiency over heuristic methods.

Contribution

It is the first to apply RL and GNNs for adaptive query vertex ordering in subgraph matching, capturing graph information for better performance.

Findings

Reduces query processing time by up to 100x

Outperforms state-of-the-art heuristic algorithms

Effectively captures graph structure for ordering

Abstract

Subgraph matching is a fundamental problem in various fields that use graph structured data. Subgraph matching algorithms enumerate all isomorphic embeddings of a query graph q in a data graph G. An important branch of matching algorithms exploit the backtracking search approach which recursively extends intermediate results following a matching order of query vertices. It has been shown that the matching order plays a critical role in time efficiency of these backtracking based subgraph matching algorithms. In recent years, many advanced techniques for query vertex ordering (i.e., matching order generation) have been proposed to reduce the unpromising intermediate results according to the preset heuristic rules. In this paper, for the first time we apply the Reinforcement Learning (RL) and Graph Neural Networks (GNNs) techniques to generate the high-quality matching order for subgraph matching algorithms. Instead of using the fixed heuristics to generate the matching order, our model could capture and make full use of the graph information, and thus determine the query vertex order with the adaptive learning-based rule that could significantly reduces the number of redundant enumerations. With the help of the reinforcement learning framework, our model is able to consider the long-term benefits rather than only consider the local information at current ordering step.Extensive experiments on six real-life data graphs demonstrate that our proposed matching order generation technique could reduce up to two orders of magnitude of query processing time compared to the state-of-the-art algorithms.