Towards Principled Graph Transformers
Fuente:
arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910688622936064 |
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| author | Müller, Luis Kusuma, Daniel Bonet, Blai Morris, Christopher |
| author_facet | Müller, Luis Kusuma, Daniel Bonet, Blai Morris, Christopher |
| contents | Graph learning architectures based on the k-dimensional Weisfeiler-Leman (k-WL) hierarchy offer a theoretically well-understood expressive power. However, such architectures often fail to deliver solid predictive performance on real-world tasks, limiting their practical impact. In contrast, global attention-based models such as graph transformers demonstrate strong performance in practice, but comparing their expressive power with the k-WL hierarchy remains challenging, particularly since these architectures rely on positional or structural encodings for their expressivity and predictive performance. To address this, we show that the recently proposed Edge Transformer, a global attention model operating on node pairs instead of nodes, has at least 3-WL expressive power. Empirically, we demonstrate that the Edge Transformer surpasses other theoretically aligned architectures regarding predictive performance while not relying on positional or structural encodings. Our code is available at https://github.com/luis-mueller/towards-principled-gts |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_10119 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Principled Graph Transformers Müller, Luis Kusuma, Daniel Bonet, Blai Morris, Christopher Machine Learning Artificial Intelligence Graph learning architectures based on the k-dimensional Weisfeiler-Leman (k-WL) hierarchy offer a theoretically well-understood expressive power. However, such architectures often fail to deliver solid predictive performance on real-world tasks, limiting their practical impact. In contrast, global attention-based models such as graph transformers demonstrate strong performance in practice, but comparing their expressive power with the k-WL hierarchy remains challenging, particularly since these architectures rely on positional or structural encodings for their expressivity and predictive performance. To address this, we show that the recently proposed Edge Transformer, a global attention model operating on node pairs instead of nodes, has at least 3-WL expressive power. Empirically, we demonstrate that the Edge Transformer surpasses other theoretically aligned architectures regarding predictive performance while not relying on positional or structural encodings. Our code is available at https://github.com/luis-mueller/towards-principled-gts |
| title | Towards Principled Graph Transformers |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2401.10119 |