Exploring Robustness of GNN against Universal Injection Attack from a Worst-case Perspective.
Dandan Ni|Sheng Zhang|Cong Deng|Han Liu|Gang Chen|Minhao Cheng|Hongyang Chen
| Anthology ID: | DBLP:conf/cikm/NiZD00CC24 |
|---|---|
| Volume: | Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, CIKM 2024, Boise, ID, USA, October 21-25, 2024 |
| Year: | 2024 |
| Venue: | International Conference on Information and Knowledge Management (CIKM) |
| Publisher: | ACM |
| Pages: | 1785-1794 |
| URL: | https://doi.org/10.1145/3627673.3679862 |
| DOI: | https://doi.org/10.1145/3627673.3679862 |
| DBLP: | conf/cikm/NiZD00CC24 |
| BibTeX: |
@inproceedings{ni-2024-exploring,
author = {Dandan Ni and
Sheng Zhang and
Cong Deng and
Han Liu and
Gang Chen and
Minhao Cheng and
Hongyang Chen},
editor = {Edoardo Serra and
Francesca Spezzano},
title = {{Exploring Robustness of GNN against Universal Injection Attack from a Worst-case Perspective}},
booktitle = {{Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, CIKM 2024, Boise, ID, USA, October 21-25, 2024}},
pages = {1785--1794},
publisher = {ACM},
year = {2024},
url = {https://doi.org/10.1145/3627673.3679862},
doi = {https://doi.org/10.1145/3627673.3679862}
}