Summary of Training a Label-noise-resistant Gnn with Reduced Complexity, by Rui Zhao et al.
Training a Label-Noise-Resistant GNN with Reduced Complexity
by Rui Zhao, Bin Shi, Zhiming Liang, Jianfei Ruan, Bo Dong, Lu Lin
First submitted to arxiv on: 17 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Social and Information Networks (cs.SI)
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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 This paper introduces a new approach to training Graph Neural Networks (GNNs) robustly against label noise in semi-supervised node classification tasks. The proposed method, called Label Ensemble Graph Neural Network (LEGNN), reframes the problem as a label ensemble task rather than relying on a single reliable label. LEGNN uses a two-step process to gather informative multiple labels and mitigate the impact of inaccurately labeled neighbors. The approach is computationally efficient and scalable, achieving outstanding performance on six datasets while ensuring efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps GNNs work better when some nodes have wrong labels. They make it easier for GNNs to learn by making sure each node has multiple correct labels instead of just one unreliable label. This way, even if some neighbors have wrong labels, the GNN can still learn from other reliable labels. The new method is fast and works well on big datasets with many nodes and edges. |
Keywords
» Artificial intelligence » Classification » Gnn » Graph neural network » Semi supervised
