Have Graph -- Will Lift? The Case for Higher-Order Benchmarks
Not on the map. Why?
Abstract (arXiv)
After a somewhat rocky start, geometry and topology have established a foothold in machine learning. Message passing, either on graphs or higher-order complexes, is one of the main drivers of geometric deep learning, and paradigms that were once considered to be firmly in the realm of the abstract-like sheaves-have been "tamed" to serve as novel inductive biases for model architectures in topological deep learning. The veritable diversity of models, however, is in stark contrast to the scarcity of suitable benchmark datasets. As a result, researchers often resort to lifting existing graph datasets to include higher-order information. In this opinion paper, I want to encourage the community to also source new datasets, which may be used to prop up the foundations of our research field.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 18 of its 34 references that are in the map. First posted on arXiv 8 May 2026.
Builds on
18 of 34 references.
References · 18
- 2018Datasheets for Datasetscited by 1,371
- 2016Geometric deep learning: going beyond Euclidean datacited by 1,042
- 2019A Fair Comparison of Graph Neural Networks for Graph Classificationcited by 244
- 2021Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networkscited by 219
- 2021Weisfeiler and Leman go Machine Learning: The Story so farcited by 121
- 2018Troubling Trends in Machine Learning Scholarshipcited by 107
- 2023Everything is Connected: Graph Neural Networkscited by 85
- 2020Graph Kernels: State-of-the-Art and Future Challengescited by 63
- 2025Position: Graph Learning Will Lose Relevance Due To Poor Benchmarkscited by 52
- 2025PyG 2.0: Scalable Learning on Real World Graphscited by 49
- 2019Persistence Homology of Networks: Methods and Applicationscited by 30
- 2024TopoBench: A Framework for Benchmarking Topological Deep Learningcited by 24
6 more
- 2025No Metric to Rule Them All: Toward Principled Evaluations of Graph-Learning Datasetscited by 16
- 2024MANTRA: The Manifold Triangulations Assemblagecited by 12
- 2024Mapping the Multiverse of Latent Representationscited by 5
- 2025HOPSE: Scalable Higher-Order Positional and Structural Encoder for Combinatorial Representationscited by 5
- 2025Demystifying Topological Message-Passing with Relational Structures: A Case Study on Oversquashing in Simplicial Message-Passingcited by 2
- 2025Graph Homomorphism Distortion: A Metric to Distinguish Them All and in the Latent Space Bind Themcited by 1
Built on by
Citing papers per year: 2026: 2.
2 papers.