divelab/GOOD
GOOD: A Graph Out-of-Distribution Benchmark [NeurIPS 2022 Datasets and Benchmarks]
This project helps researchers and machine learning engineers evaluate how well graph-based AI models perform when encountering new, unexpected data patterns. It takes various graph datasets as input and outputs performance metrics for different models, indicating their generalization ability. It's designed for anyone working with graph neural networks who needs to assess model robustness to real-world data shifts.
207 stars. No commits in the last 6 months.
Use this if you need to benchmark the out-of-distribution generalization capabilities of graph deep learning algorithms across diverse graph datasets and data shifts.
Not ideal if you are looking for a pre-trained, production-ready graph model or a tool for general graph data analysis without a focus on out-of-distribution performance.
Stars
207
Forks
22
Language
Python
License
GPL-3.0
Category
Last pushed
Feb 21, 2025
Commits (30d)
0
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