Summary of Adagmlp: Adaboosting Gnn-to-mlp Knowledge Distillation, by Weigang Lu et al.
AdaGMLP: AdaBoosting GNN-to-MLP Knowledge Distillation
by Weigang Lu, Ziyu Guan, Wei Zhao, Yaming Yang
First submitted to arxiv on: 23 May 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 proposed AdaGMLP framework addresses the challenges of insufficient training data and incomplete test data in GNN-to-MLP knowledge distillation. It leverages an ensemble of diverse MLP students trained on different subsets of labeled nodes, which enhances the robustness of the student model. Additionally, it incorporates a Node Alignment technique to improve predictions when test data is missing or incomplete features. The framework outperforms existing G2M methods on seven benchmark datasets with different settings, making it suitable for latency-sensitive real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AdaGMLP is a new way to make graph neural networks (GNNs) work better and faster on devices that need to process information quickly. GNNs are good at processing complex data like social networks or molecules, but they can be slow because they require a lot of computation. To fix this, researchers developed ways to “distill” the knowledge from GNNs into smaller models called MLPs (multi-layer perceptrons) that can run faster and use less power. However, these methods didn’t work well when there wasn’t enough training data or when test data was incomplete. The new AdaGMLP framework fixes these problems by using multiple MLP students trained on different parts of the data to make predictions. It also helps align missing features in test data to improve accuracy. |
Keywords
» Artificial intelligence » Alignment » Gnn » Knowledge distillation » Student model
