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Graph neural networks (GNNs) have attracted much attention due to theirability to leverage the intrinsic geometries of the underlying data. Althoughmany different types of GNN models have been developed, with many benchmarkingprocedures to demonstrate the superiority of one GNN model over the others,there is a lack of systematic understanding of the underlying benchmarkingdatasets, and what aspects of the model are being tested. Here, we provide aprincipled approach to taxonomize graph benchmarking datasets by carefullydesigning a collection of graph perturbations to probe the essential datacharacteristics that GNN models leverage to perform predictions. Ourdata-driven taxonomization of graph datasets provides a new understanding ofcritical dataset characteristics that will enable better model evaluation andthe development of more specialized GNN models.

Towards a Taxonomy of Graph Learning Datasets