Towards Fair Graph Anomaly Detection: Problem, Benchmark Datasets, and Evaluation.
Neng Kai Nigel Neo|Yeon-Chang Lee|Yiqiao Jin|Sang-Wook Kim|Srijan Kumar
| Anthology ID: | DBLP:conf/cikm/NeoLJKK24 |
|---|---|
| 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: | 1752-1762 |
| URL: | https://doi.org/10.1145/3627673.3679754 |
| DOI: | https://doi.org/10.1145/3627673.3679754 |
| DBLP: | conf/cikm/NeoLJKK24 |
| BibTeX: |
@inproceedings{neo-2024-towards,
author = {Neng Kai Nigel Neo and
Yeon-Chang Lee and
Yiqiao Jin and
Sang-Wook Kim and
Srijan Kumar},
editor = {Edoardo Serra and
Francesca Spezzano},
title = {{Towards Fair Graph Anomaly Detection: Problem, Benchmark Datasets, and Evaluation}},
booktitle = {{Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, CIKM 2024, Boise, ID, USA, October 21-25, 2024}},
pages = {1752--1762},
publisher = {ACM},
year = {2024},
url = {https://doi.org/10.1145/3627673.3679754},
doi = {https://doi.org/10.1145/3627673.3679754}
}