Summary of Unraveling the Impact Of Heterophilic Structures on Graph Positive-unlabeled Learning, by Yuhao Wu et al.
Unraveling the Impact of Heterophilic Structures on Graph Positive-Unlabeled Learning
by Yuhao Wu, Jiangchao Yao, Bo Han, Lina Yao, Tongliang Liu
First submitted to arxiv on: 30 May 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 The research paper introduces a new method for Positive-Unlabeled (PU) learning on graph data, which has applications in real-world scenarios where PU learning is crucial. The proposed method, Graph PU Learning with Label Propagation Loss (GPL), addresses the challenge of edge heterophily, which violates the irreducibility assumption and affects classifier training. GPL reduces heterophily through an intermediate step, optimizing a bilevel problem that balances heterophily reduction and classifier learning. Experimental results on various datasets demonstrate significant performance improvements over baseline methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Graph PU Learning with Label Propagation Loss (GPL) is a new method for learning from Positive-Unlabeled data on graphs. It solves a challenge called edge heterophily, which makes it hard to train a good classifier. GPL reduces this problem by first making the graph more similar and then training the classifier. This helps a lot and makes GPL better than other methods. |
