On Structural Expressive Power of Graph Transformers

Author:

Zhu Wenhao1ORCID,Wen Tianyu1ORCID,Song Guojie1ORCID,Wang Liang2ORCID,Zheng Bo2ORCID

Affiliation:

1. Peking University, Beijing, China

2. Alibaba Group, Beijing, China

Funder

National Natural Science Foundation of China

Publisher

ACM

Reference50 articles.

1. On Weisfeiler-Leman invariance: Subgraph counts and related graph properties

2. Waiss Azizian and Marc Lelarge . 2020. Expressive power of invariant and equivariant graph neural networks. arXiv preprint arXiv:2006.15646 ( 2020 ). Waiss Azizian and Marc Lelarge. 2020. Expressive power of invariant and equivariant graph neural networks. arXiv preprint arXiv:2006.15646 (2020).

3. Giorgos Bouritsas , Fabrizio Frasca , Stefanos P Zafeiriou , and Michael Bronstein . 2022. Improving graph neural network expressivity via subgraph isomorphism counting . IEEE Transactions on Pattern Analysis and Machine Intelligence ( 2022 ). Giorgos Bouritsas, Fabrizio Frasca, Stefanos P Zafeiriou, and Michael Bronstein. 2022. Improving graph neural network expressivity via subgraph isomorphism counting. IEEE Transactions on Pattern Analysis and Machine Intelligence (2022).

4. Dexiong Chen , Leslie O'Bray , and Karsten Borgwardt . 2022 . Structure-aware transformer for graph representation learning . In International Conference on Machine Learning. PMLR, 3469--3489 . Dexiong Chen, Leslie O'Bray, and Karsten Borgwardt. 2022. Structure-aware transformer for graph representation learning. In International Conference on Machine Learning. PMLR, 3469--3489.

5. Principal neighbourhood aggregation for graph nets;Corso Gabriele;Advances in Neural Information Processing Systems,2020

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