GraphBench: Next-generation graph learning benchmarking
Fuente:
arXiv
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| Autori principali: | , , , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866910203916582912 |
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| author | Stoll, Timo Qian, Chendi Finkelshtein, Ben Parviz, Ali Weber, Darius Frasca, Fabrizio Shavit, Hadar Siraudin, Antoine Mielke, Arman Anastacio, Marie Müller, Erik Bechler-Speicher, Maya Bronstein, Michael Galkin, Mikhail Hoos, Holger Niepert, Mathias Perozzi, Bryan Tönshoff, Jan Morris, Christopher |
| author_facet | Stoll, Timo Qian, Chendi Finkelshtein, Ben Parviz, Ali Weber, Darius Frasca, Fabrizio Shavit, Hadar Siraudin, Antoine Mielke, Arman Anastacio, Marie Müller, Erik Bechler-Speicher, Maya Bronstein, Michael Galkin, Mikhail Hoos, Holger Niepert, Mathias Perozzi, Bryan Tönshoff, Jan Morris, Christopher |
| contents | Machine learning on graphs has made substantial progress across domains such as molecular property prediction and chip design. Yet benchmarking practices remain fragmented, often relying on narrow, task-specific datasets and inconsistent evaluation protocols, hindering reproducibility and broader progress. With the recent popularity of graph foundation models, these weaknesses have become apparent, as existing benchmarks are insufficient for thorough evaluation. To address these challenges, we introduce GraphBench, a comprehensive benchmark suite spanning diverse real-world domains and task settings, including node-level, edge-level, graph-level, and generative tasks. GraphBench provides standardized evaluation protocols, including consistent dataset splits and metrics for assessing out-of-distribution generalization across selected tasks, as well as a unified hyperparameter-tuning framework. We further evaluate GraphBench with recent message-passing neural networks and graph transformer models, establishing principled baselines for future research. See www.graphbench.io for further details. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_04475 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GraphBench: Next-generation graph learning benchmarking Stoll, Timo Qian, Chendi Finkelshtein, Ben Parviz, Ali Weber, Darius Frasca, Fabrizio Shavit, Hadar Siraudin, Antoine Mielke, Arman Anastacio, Marie Müller, Erik Bechler-Speicher, Maya Bronstein, Michael Galkin, Mikhail Hoos, Holger Niepert, Mathias Perozzi, Bryan Tönshoff, Jan Morris, Christopher Machine Learning Artificial Intelligence Neural and Evolutionary Computing Machine learning on graphs has made substantial progress across domains such as molecular property prediction and chip design. Yet benchmarking practices remain fragmented, often relying on narrow, task-specific datasets and inconsistent evaluation protocols, hindering reproducibility and broader progress. With the recent popularity of graph foundation models, these weaknesses have become apparent, as existing benchmarks are insufficient for thorough evaluation. To address these challenges, we introduce GraphBench, a comprehensive benchmark suite spanning diverse real-world domains and task settings, including node-level, edge-level, graph-level, and generative tasks. GraphBench provides standardized evaluation protocols, including consistent dataset splits and metrics for assessing out-of-distribution generalization across selected tasks, as well as a unified hyperparameter-tuning framework. We further evaluate GraphBench with recent message-passing neural networks and graph transformer models, establishing principled baselines for future research. See www.graphbench.io for further details. |
| title | GraphBench: Next-generation graph learning benchmarking |
| topic | Machine Learning Artificial Intelligence Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2512.04475 |