Theoretically Improving Graph Neural Networks via Anonymous Walk Graph Kernels-Reference-Cited by-同舟云学术

Theoretically Improving Graph Neural Networks via Anonymous Walk Graph Kernels

Published:2021-04-19 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Web Conference 2021
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Author:

Long Qingqing¹,Jin Yilun²,Wu Yi¹,Song Guojie¹

Affiliation:

1. Peking University, China

2. The Hong Kong University of Science and Technology, Hong Kong

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3442381.3449951

Reference52 articles.

1. DDGK: Learning Graph Representations for Deep Divergence Graph Kernels

2. Vikraman Arvind Frank Fuhlbrück Johannes Köbler and Oleg Verbitsky. 2020. On Weisfeiler-Leman invariance: subgraph counts and related graph properties. J. Comput. System Sci.(2020). Vikraman Arvind Frank Fuhlbrück Johannes Köbler and Oleg Verbitsky. 2020. On Weisfeiler-Leman invariance: subgraph counts and related graph properties. J. Comput. System Sci.(2020).

3. The Dynamics of Message Passing on Dense Graphs, with Applications to Compressed Sensing

4. Shortest-Path Kernels on Graphs

5. Giorgos Bouritsas Fabrizio Frasca Stefanos Zafeiriou and Michael M Bronstein. 2020. Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting. arXiv preprint arXiv:2006.09252(2020). Giorgos Bouritsas Fabrizio Frasca Stefanos Zafeiriou and Michael M Bronstein. 2020. Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting. arXiv preprint arXiv:2006.09252(2020).

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