Summary of Training Graph Neural Networks Using Non-robust Samples, by Yongyu Wang
Training Graph Neural Networks Using Non-Robust Samples
by Yongyu Wang
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposes a novel approach for selecting noise-sensitive training samples in Graph Neural Networks (GNNs) to improve their performance in noisy environments. GNNs leverage both graph structure and feature matrix to optimize their feature representation, but this also makes them more susceptible to noise. The proposed method constructs a smaller yet more effective training set by selecting these noise-sensitive samples from the original training set. This is evaluated on three classical GNN models (GCN, GAT, GraphSAGE) and three benchmark datasets (Cora, Citeseer, PubMed). Results show that this approach can significantly boost training performance compared to using randomly sampled training sets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GNNs are special types of neural networks that help computers understand relationships between things. They’re really good at it too! But sometimes they get confused because the data isn’t perfect. To fix this, scientists came up with a new way to pick and choose which training samples to use when teaching GNNs. They tested this on some popular models and datasets, and it worked amazingly well! This means that computers might be able to learn even better from noisy or imperfect data in the future. |
Keywords
» Artificial intelligence » Gcn » Gnn
