Dataset and Code for A Graph-Neural-Network-Powered Solver Framework

The dataset referenced herein pertains to the robust framework we introduced in our scholarly article titled, "A Graph-Neural-Network-Powered Solver Framework for Graph Optimization Problems." This comprehensive dataset is categorized into three segments: training data, verification data, and test data. Each dataset plays an integral role in the functionality and optimization of the proposed framework. The training data aids in formulating the GNN model, the verification data is used for fine-tuning the model, and the test data assesses the model's performance in test scenarios. Notably, these datasets are not a static entity; they can be reproduced using the graph model outlined in our paper. This dynamism allows for continual model optimization and provides an avenue for others in the scientific community to replicate our work, thereby fostering open and reproducible research.

本数据集所指涉者为我们在学术论文《基于图神经网络求解图优化问题的鲁棒框架》中提出的框架。该数据集结构严谨，分为训练数据、验证数据和测试数据三个部分，每一部分在框架的功能优化中均发挥着不可或缺的作用。训练数据用于构建 GNN 模型，验证数据用于模型的微调，测试数据则评估模型在测试场景中的表现。值得关注的是，这些数据集并非一成不变，它们可以通过论文中概述的图模型进行重现。这种动态性不仅促进了模型的持续优化，也为科学界同仁提供了复制我们研究工作的途径，从而推动了开放和可复现研究的进程。