CurvDrop: A Ricci Curvature Based Approach to Prevent Graph Neural Networks from Over-Smoothing and Over-Squashing.
Yang Liu|Chuan Zhou|Shirui Pan|Jia Wu|Zhao Li|Hongyang Chen|Peng Zhang
| Anthology ID: | DBLP:conf/www/Liu0P00C023 |
|---|---|
| Volume: | Proceedings of the ACM Web Conference 2023, WWW 2023, Austin, TX, USA, 30 April 2023 - 4 May 2023 |
| Year: | 2023 |
| Venue: | The Web Conference (WWW) |
| Publisher: | ACM |
| Pages: | 221-230 |
| URL: | https://doi.org/10.1145/3543507.3583269 |
| DOI: | https://doi.org/10.1145/3543507.3583269 |
| DBLP: | conf/www/Liu0P00C023 |
| BibTeX: |
@inproceedings{liu-2023-curvdrop,
author = {Yang Liu and
Chuan Zhou and
Shirui Pan and
Jia Wu and
Zhao Li and
Hongyang Chen and
Peng Zhang},
editor = {Ying Ding and
Jie Tang and
Juan F. Sequeda and
Lora Aroyo and
Carlos Castillo and
Geert-Jan Houben},
title = {{CurvDrop: A Ricci Curvature Based Approach to Prevent Graph Neural Networks from Over-Smoothing and Over-Squashing}},
booktitle = {{Proceedings of the ACM Web Conference 2023, WWW 2023, Austin, TX, USA, 30 April 2023 - 4 May 2023}},
pages = {221--230},
publisher = {ACM},
year = {2023},
url = {https://doi.org/10.1145/3543507.3583269},
doi = {https://doi.org/10.1145/3543507.3583269}
}