RSGNN: A Model-agnostic Approach for Enhancing the Robustness of Signed Graph Neural Networks.

@inproceedings{zhang-2023-rsgnn, author = {Zeyu Zhang and Jiamou Liu and Xianda Zheng and Yifei Wang and Pengqian Han and Yupan Wang and Kaiqi Zhao and Zijian Zhang}, editor = {Ying Ding and Jie Tang and Juan F. Sequeda and Lora Aroyo and Carlos Castillo and Geert-Jan Houben}, title = {{RSGNN: A Model-agnostic Approach for Enhancing the Robustness of Signed Graph Neural Networks}}, booktitle = {{Proceedings of the ACM Web Conference 2023, WWW 2023, Austin, TX, USA, 30 April 2023 - 4 May 2023}}, pages = {60--70}, publisher = {ACM}, year = {2023}, url = {https://doi.org/10.1145/3543507.3583221}, doi = {https://doi.org/10.1145/3543507.3583221} }