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Taxonomy of Benchmarks in Graph Representation Learning.
In: (Proceedings of Machine Learning Research). 2022. accepted (Proceedings of Machine Learning Research ; 198)
Graph Neural Networks (GNNs) extend the success of neural networks to graph-structured data by accounting for their intrinsic geometry. While extensive research has been done on developing GNN models with superior performance according to a collection of graph representation learning benchmarks, it is currently not well understood what aspects of a given model are probed by them. For example, to what extent do they test the ability of a model to leverage graph structure vs. node features? Here, we develop a principled approach to taxonomize benchmarking datasets according to a sensitivity profile that is based on how much GNN performance changes due to a collection of graph perturbations. Our data-driven analysis provides a deeper understanding of which benchmarking data characteristics are leveraged by GNNs. Consequently, our taxonomy can aid in selection and development of adequate graph benchmarks, and better informed evaluation of future GNN methods. Finally, our approach and implementation in GTaxoGym package1 are extendable to multiple graph prediction task types and future datasets.
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Publication type
Article: Conference contribution
Language
english
Publication Year
2022
HGF-reported in Year
2022
Conference Title
Proceedings of Machine Learning Research
Quellenangaben
Volume: 198
Institute(s)
Institute of AI for Health (AIH)
POF-Topic(s)
30205 - Bioengineering and Digital Health
Research field(s)
Enabling and Novel Technologies
PSP Element(s)
G-540003-001
Scopus ID
85164538462
Erfassungsdatum
2023-10-18