graphdeeplearning/benchmarking-gnns
Repository for benchmarking graph neural networks (JMLR 2023)
This project helps researchers and developers evaluate the performance of different graph neural networks (GNNs) on various tasks. It takes standard graph datasets, such as molecular structures (ZINC, AQSOL) or theoretical graph properties (GraphTheoryProp, CYCLES), and outputs benchmark results, allowing for direct comparison of GNN architectures. This is for machine learning researchers, data scientists, and computational chemists who develop or apply GNNs.
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Use this if you need to rigorously compare different graph neural network models on diverse datasets to understand their strengths and weaknesses for specific applications.
Not ideal if you are looking for a pre-trained model for immediate inference or if you do not have experience working with machine learning benchmarks and model evaluation.
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Jun 22, 2023
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