AAAI · 2026
Adaptive Riemannian Graph Neural Networks
Proceedings of the AAAI Conference on Artificial Intelligence
How can a graph model adapt to different local geometries?
ARGNN learns a node specific Riemannian metric instead of imposing one fixed curvature on an entire graph. A diagonal metric parameterization and geometric regularization support efficient training, with convergence analysis and experiments on homophilic and heterophilic graphs.
Research themes
Cite this paper
Xudong Wang, Chris Ding, Tongxin Li, Jicong Fan. Adaptive Riemannian Graph Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 2026. https://doi.org/10.1609/aaai.v40i31.39869
BibTeX
@inproceedings{tongxin-argnn,
title = {{Adaptive Riemannian Graph Neural Networks}},
author = {Xudong Wang and Chris Ding and Tongxin Li and Jicong Fan},
year = {2026},
booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
url = {https://doi.org/10.1609/aaai.v40i31.39869},
doi = {10.1609/aaai.v40i31.39869}
}
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