  Recently, there has been an increasing interest in (supervised) learning with
graph data, especially using graph neural networks. However, the development of
meaningful benchmark datasets and standardized evaluation procedures is
lagging, consequently hindering advancements in this area. To address this, we
introduce the TUDataset for graph classification and regression. The collection
consists of over 120 datasets of varying sizes from a wide range of
applications. We provide Python-based data loaders, kernel and graph neural
network baseline implementations, and evaluation tools. Here, we give an
overview of the datasets, standardized evaluation procedures, and provide
baseline experiments. All datasets are available at www.graphlearning.io. The
experiments are fully reproducible from the code available at
www.github.com/chrsmrrs/tudataset.
