Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks
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arXiv
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| Hauptverfasser: | , , , , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| author | Bechler-Speicher, Maya Finkelshtein, Ben Frasca, Fabrizio Müller, Luis Tönshoff, Jan Siraudin, Antoine Zaverkin, Viktor Bronstein, Michael M. Niepert, Mathias Perozzi, Bryan Galkin, Mikhail Morris, Christopher |
| author_facet | Bechler-Speicher, Maya Finkelshtein, Ben Frasca, Fabrizio Müller, Luis Tönshoff, Jan Siraudin, Antoine Zaverkin, Viktor Bronstein, Michael M. Niepert, Mathias Perozzi, Bryan Galkin, Mikhail Morris, Christopher |
| contents | While machine learning on graphs has demonstrated promise in drug design and molecular property prediction, significant benchmarking challenges hinder its further progress and relevance. Current benchmarking practices often lack focus on transformative, real-world applications, favoring narrow domains like two-dimensional molecular graphs over broader, impactful areas such as combinatorial optimization, relational databases, or chip design. Additionally, many benchmark datasets poorly represent the underlying data, leading to inadequate abstractions and misaligned use cases. Fragmented evaluations and an excessive focus on accuracy further exacerbate these issues, incentivizing overfitting rather than fostering generalizable insights. These limitations have prevented the development of truly useful graph foundation models. This position paper calls for a paradigm shift toward more meaningful benchmarks, rigorous evaluation protocols, and stronger collaboration with domain experts to drive impactful and reliable advances in graph learning research, unlocking the potential of graph learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_14546 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks Bechler-Speicher, Maya Finkelshtein, Ben Frasca, Fabrizio Müller, Luis Tönshoff, Jan Siraudin, Antoine Zaverkin, Viktor Bronstein, Michael M. Niepert, Mathias Perozzi, Bryan Galkin, Mikhail Morris, Christopher Machine Learning Artificial Intelligence Neural and Evolutionary Computing While machine learning on graphs has demonstrated promise in drug design and molecular property prediction, significant benchmarking challenges hinder its further progress and relevance. Current benchmarking practices often lack focus on transformative, real-world applications, favoring narrow domains like two-dimensional molecular graphs over broader, impactful areas such as combinatorial optimization, relational databases, or chip design. Additionally, many benchmark datasets poorly represent the underlying data, leading to inadequate abstractions and misaligned use cases. Fragmented evaluations and an excessive focus on accuracy further exacerbate these issues, incentivizing overfitting rather than fostering generalizable insights. These limitations have prevented the development of truly useful graph foundation models. This position paper calls for a paradigm shift toward more meaningful benchmarks, rigorous evaluation protocols, and stronger collaboration with domain experts to drive impactful and reliable advances in graph learning research, unlocking the potential of graph learning. |
| title | Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks |
| topic | Machine Learning Artificial Intelligence Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2502.14546 |