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} }