GQ: Towards Generalizable Deep Q-Learning for Steiner Tree in Graphs

• Published in: Proceedings (IEEE International Conference on Data Mining) pp. 141 - 150
• Main Authors: Huang, Wei, Wang, Hanchen, Wen, Dong, Chen, Xuefeng, Zhang, Wenjie, Zhang, Ying
• Format: Conference Proceeding
• Language: English
• Published: IEEE 09.12.2024
• Subjects: A Search; Computational efficiency; Data mining; Graph Neural Networks; Neural networks; Optimization; Q-learning; Reinforcement Learning; Steiner Tree; Steiner trees; Synthetic data; Time complexity
• ISSN: 2374-8486
• DOI: 10.1109/ICDM59182.2024.00021

Finding the optimal Steiner Tree in graphs has been a critical combinatorial optimization challenge that finds widespread applications in network design. Despite its importance, finding the optimal Steiner Tree remains computationally expensive, especially for large graphs due to its NP-hard nature. Traditional approaches often suffer from high time complexity or poor approximation ratio. Machine learning approaches often leverage local graph structure information instead of global graph structure information, and often suffer from the generalization ability issues in practice. In this paper, we propose a novel reinforcement learning based framework to solve STP, in which we reformulate the classical Q-value computation to capture both global graph structure and deterministic information to search for the Steiner tree. Experiments on both synthetic and real-world datasets demonstrate that our framework exhibits better generalization ability compared to the existing machine learning methods, where our framework can be trained on the small graphs and generalize well to larger graphs and the graphs from different distributions.