June 6, 2024, 4:42 a.m. | Luis M\"uller, Christopher Morris

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.03148v1 Announce Type: new
Abstract: Graph neural network architectures aligned with the $k$-dimensional Weisfeiler--Leman ($k$-WL) hierarchy offer theoretically well-understood expressive power. However, these architectures often fail to deliver state-of-the-art predictive performance on real-world graphs, limiting their practical utility. While recent works aligning graph transformer architectures with the $k$-WL hierarchy have shown promising empirical results, employing transformers for higher orders of $k$ remains challenging due to a prohibitive runtime and memory complexity of self-attention as well as impractical architectural assumptions, such …

arxiv cs.lg transformers type

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