Summary of Cooperative Classification and Rationalization For Graph Generalization, by Linan Yue et al.
Cooperative Classification and Rationalization for Graph Generalization
by Linan Yue, Qi Liu, Ye Liu, Weibo Gao, Fangzhou Yao, Wenfeng Li
First submitted to arxiv on: 10 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 Cooperative Classification and Rationalization (C2R) method addresses the challenges of generalizing Graph Neural Networks (GNNs) to out-of-distribution data. The C2R approach consists of a classification module that assumes multiple environments are available, and a rationalization module that identifies relevant rationale subgraphs and de-correlates non-rationale subgraphs with labels. By aligning graph representations from the classification module with rationale subgraph representations using knowledge distillation methods, the learning signal for rationales is enhanced. The C2R method also incorporates multiple environments into the classification module for cooperative learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Cooperative Classification and Rationalization (C2R) method helps Graph Neural Networks (GNNs) work better when they see new data that’s different from what they were trained on. This is a big problem, but C2R solves it by teaching GNNs to understand which parts of the data are most important. It does this by giving the GNN two tasks: one is to classify the data, and the other is to figure out why the GNN made that classification. By doing these two things together, C2R makes sure that GNNs can learn from new data even if it’s different from what they’ve seen before. |
Keywords
* Artificial intelligence * Classification * Gnn * Knowledge distillation
