利用图神经网络辅助蒙特卡洛树搜索求解近似最优Steiner树
Nearly Optimal Steiner Trees using Graph Neural Network Assisted Monte Carlo Tree Search
AI总结：
针对Steiner树求解问题，提出结合图神经网络与蒙特卡洛树搜索的方法，训练以部分解为输入的图神经网络指导搜索，在多类图上优于标准2-近似算法，常能求得最优解。
摘要：
图神经网络在学习类问题，以及子图同构问题、旅行商问题等组合优化与图论问题中均有实用价值。本文提出一种结合图神经网络与蒙特卡洛树搜索求解Steiner树的方法：首先训练一个以部分解为输入、输出待添加新节点的图神经网络，再将该神经网络用于蒙特卡洛搜索中计算Steiner树。在多种不同类型的图上，该方法的表现始终优于标准的2-近似算法，且常能求得最优解。
英文摘要：
Graph neural networks are useful for learning problems, as well as for combinatorial and graph problems such as the Subgraph Isomorphism Problem and the Traveling Salesman Problem. We describe an approach for computing Steiner Trees by combining a graph neural network and Monte Carlo Tree Search. We first train a graph neural network that takes as input a partial solution and proposes a new node to be added as output. This neural network is then used in a Monte Carlo search to compute a Steiner tree. The proposed method consistently outperforms the standard 2-approximation algorithm on many different types of graphs and often finds the optimal solution.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
