THUMNLab/awesome-graph-ood
Papers about out-of-distribution generalization on graphs.
This resource curates academic papers focused on methods for training machine learning models that work well with graph-structured data (like social networks or molecular structures), even when the data encountered in the real world is different from the training data. It categorizes research into approaches based on how data is handled, how models are designed, or what learning strategies are used. Data scientists and machine learning researchers who build and deploy models on graph data would use this to understand the latest techniques for ensuring their models are robust and generalize effectively.
168 stars. No commits in the last 6 months.
Use this if you are a machine learning researcher or data scientist working with graph neural networks and need to ensure your models perform reliably on new, unseen graph data that might have different characteristics from your training datasets.
Not ideal if you are looking for introductory material on graph neural networks or an off-the-shelf software tool for immediate use.
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MIT
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Last pushed
Jun 05, 2023
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