Summary of Improving Out-of-distribution Generalization in Graphs Via Hierarchical Semantic Environments, by Yinhua Piao et al.
Improving out-of-distribution generalization in graphs via hierarchical semantic environments
by Yinhua Piao, Sangseon Lee, Yijingxiu Lu, Sun Kim
First submitted to arxiv on: 4 Mar 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 approach generates hierarchical semantic environments for each graph to enhance graph invariant learning and handle distribution shifts in out-of-distribution (OOD) generalization. The method extracts variant subgraphs from input graphs to generate proxy predictions on local environments, then employs stochastic attention mechanisms to re-extract subgraphs for regenerating global environments. A new learning objective guides the model to learn environment diversity while maintaining consistency across hierarchies. This framework is demonstrated to be effective in real-world graph data and achieves improvements over baselines on OOD generalization tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem with how computers understand graphs, which are important for many applications like medicine and social networks. The challenge is that when we train these graph- understanding models on one set of data, they often don’t work well on new, different data. To fix this, the authors create special environments for each graph that help the model learn to be more consistent. They use a combination of old and new ideas to make these environments and test them on real-world data. The results show that their approach is better than others at making accurate predictions. |
Keywords
* Artificial intelligence * Attention * Generalization
