Graph neural networks (GNNs) have attracted much attention due to their ability to leverage the intrinsic geometries of the underlying data. Although many different types of GNN models have been developed, with many benchmarking procedures to demonstrate the superiority of one GNN model over the others, there is a lack of systematic understanding of the underlying benchmarking datasets, and what aspects of the model are being tested. Here, we provide a principled approach to taxonomize graph benchmarking datasets by carefully designing a collection of graph perturbations to probe the essential data characteristics that GNN models leverage to perform predictions. Our data-driven taxonomization of graph datasets provides a new understanding of critical dataset characteristics that will enable better model evaluation and the development of more specialized GNN models.
@article{arxiv.2110.14809,
title = {Towards a Taxonomy of Graph Learning Datasets},
author = {Renming Liu and Semih Cantürk and Frederik Wenkel and Dylan Sandfelder and Devin Kreuzer and Anna Little and Sarah McGuire and Leslie O'Bray and Michael Perlmutter and Bastian Rieck and Matthew Hirn and Guy Wolf and Ladislav Rampášek},
journal= {arXiv preprint arXiv:2110.14809},
year = {2021}
}