Summary of Leveraging Invariant Principle For Heterophilic Graph Structure Distribution Shifts, by Jinluan Yang et al.
Leveraging Invariant Principle for Heterophilic Graph Structure Distribution Shifts
by Jinluan Yang, Zhengyu Chen, Teng Xiao, Wenqiao Zhang, Yong Lin, Kun Kuang
First submitted to arxiv on: 18 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 HGNNs have shown promise for semi-supervised learning on graphs, but real-world graphs often exhibit complex neighbor patterns and local structures. While previous works focused on designing better HGNN backbones or architectures for node classification tasks, they neglected the impact of structure differences between training and testing nodes. To address this, we propose HEI, a framework that generates invariant node representations by incorporating heterophily information to infer latent environments without data augmentation. Our method can achieve guaranteed performance under heterophilic graph structure distribution shifts. Extensive experiments on various benchmarks and backbones demonstrate the effectiveness of our approach compared to existing state-of-the-art baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary HGNNs are a type of AI model that helps computers understand relationships between things on graphs, like social networks or molecules. They’re really good at learning from incomplete data, but real-world graphs can be super complicated. Most people focus on making these models better, but they ignore how the structure of the graph changes when moving from training to testing. Our new approach, called HEI, helps computers understand these complex structures and makes predictions that work well even when the graph changes. |
Keywords
» Artificial intelligence » Classification » Data augmentation » Semi supervised
