Summary of Applying Graph Neural Network to Supplygraph For Supply Chain Network, by Kihwan Han
Applying graph neural network to SupplyGraph for supply chain network
by Kihwan Han
First submitted to arxiv on: 23 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents an assessment of the SupplyGraph dataset, a publicly available dataset for supply chain networks. The authors aim to provide clarity on the dataset’s description, data quality assurance process, and machine learning model specifications. They compare the performance of Multilayer Perceptions (MLP), Graph Convolution Network (GCN), and Graph Attention Network (GAT) on a demanding forecasting task, using matching hyperparameters as feasible as possible. The results show that GAT performed best, followed by GCN and MLP, with statistically significant improvements at α = 0.05 after correction for multiple comparisons. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The SupplyGraph dataset is used to study supply chain networks. The researchers examine the quality of this data and how well different machine learning models work on it. They test three types of models: Multi-Layer Perceptions, Graph Convolution Networks, and Graph Attention Networks. One model does better than the others at predicting what will happen in a supply chain network. This study helps make future research in this area more reliable. |
Keywords
» Artificial intelligence » Attention » Gcn » Graph attention network » Machine learning
