Towards a Taxonomy of Graph Learning Datasets

• URL: http://arxiv.org/abs/2110.14809v1
• Date: Wed, 27 Oct 2021 23:08:01 GMT
• Title: Towards a Taxonomy of Graph Learning Datasets
• Authors: Renming Liu, Semih Cant\"urk, Frederik Wenkel, Dylan Sandfelder, Devin Kreuzer, Anna Little, Sarah McGuire, Leslie O'Bray, Michael Perlmutter, Bastian Rieck, Matthew Hirn, Guy Wolf and Ladislav Ramp\'a\v{s}ek
• Abstract summary: Graph neural networks (GNNs) have attracted much attention due to their ability to leverage the intrinsic geometries of the underlying data. Here, we provide a principled approach to taxonomize graph benchmarking datasets by carefully designing a collection of graph perturbations. Our data-driven taxonomization of graph datasets provides a new understanding of critical dataset characteristics.
• Score: 10.151886932716518
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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