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.

Dataset Files

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[1] Congsong Zhang, "Dataset and Code for A Graph-Neural-Network-Powered Solver Framework", IEEE Dataport, 2023. [Online]. Available: http://dx.doi.org/10.21227/nzn9-td08. Accessed: Dec. 26, 2024.
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doi = {10.21227/nzn9-td08},
url = {http://dx.doi.org/10.21227/nzn9-td08},
author = {Congsong Zhang },
publisher = {IEEE Dataport},
title = {Dataset and Code for A Graph-Neural-Network-Powered Solver Framework},
year = {2023} }
TY - DATA
T1 - Dataset and Code for A Graph-Neural-Network-Powered Solver Framework
AU - Congsong Zhang
PY - 2023
PB - IEEE Dataport
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Congsong Zhang. (2023). Dataset and Code for A Graph-Neural-Network-Powered Solver Framework. IEEE Dataport. http://dx.doi.org/10.21227/nzn9-td08
Congsong Zhang, 2023. Dataset and Code for A Graph-Neural-Network-Powered Solver Framework. Available at: http://dx.doi.org/10.21227/nzn9-td08.
Congsong Zhang. (2023). "Dataset and Code for A Graph-Neural-Network-Powered Solver Framework." Web.
1. Congsong Zhang. Dataset and Code for A Graph-Neural-Network-Powered Solver Framework [Internet]. IEEE Dataport; 2023. Available from : http://dx.doi.org/10.21227/nzn9-td08
Congsong Zhang. "Dataset and Code for A Graph-Neural-Network-Powered Solver Framework." doi: 10.21227/nzn9-td08