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Recently, there has been an increasing interest in (supervised) learning withgraph data, especially using graph neural networks. However, the development ofmeaningful benchmark datasets and standardized evaluation procedures islagging, consequently hindering advancements in this area. To address this, weintroduce the TUDataset for graph classification and regression. The collectionconsists of over 120 datasets of varying sizes from a wide range ofapplications. We provide Python-based data loaders, kernel and graph neuralnetwork baseline implementations, and evaluation tools. Here, we give anoverview of the datasets, standardized evaluation procedures, and providebaseline experiments. All datasets are available at www.graphlearning.io. Theexperiments are fully reproducible from the code available atwww.github.com/chrsmrrs/tudataset.

TUDataset: A collection of benchmark datasets for learning with graphs