FreeGAD: A Training-Free yet Effective Approach for Graph Anomaly Detection.
Yunfeng Zhao|Yixin Liu|Shiyuan Li|Qingfeng Chen|Yu Zheng|Shirui Pan
| Anthology ID: | DBLP:conf/cikm/Zhao0LC0P25 |
|---|---|
| Volume: | Proceedings of the 34th ACM International Conference on Information and Knowledge Management, CIKM 2025, Seoul, Republic of Korea, November 10-14, 2025 |
| Year: | 2025 |
| Venue: | International Conference on Information and Knowledge Management (CIKM) |
| Publisher: | ACM |
| Pages: | 4379-4389 |
| URL: | https://doi.org/10.1145/3746252.3761125 |
| DOI: | https://doi.org/10.1145/3746252.3761125 |
| DBLP: | conf/cikm/Zhao0LC0P25 |
| BibTeX: |
@inproceedings{zhao-2025-freegad,
author = {Yunfeng Zhao and
Yixin Liu and
Shiyuan Li and
Qingfeng Chen and
Yu Zheng and
Shirui Pan},
editor = {Meeyoung Cha and
Carl Yang and
Senjuti Basu Roy and
Chanyoung Park and
Zhenhui Jessie Li and
Noseong Park and
Carl Yang and
Senjuti Basu Roy and
Zhenhui Jessie Li and
Jaap Kamps and
Kijung Shin and
Bryan Hooi and
Lifang He},
title = {{FreeGAD: A Training-Free yet Effective Approach for Graph Anomaly Detection}},
booktitle = {{Proceedings of the 34th ACM International Conference on Information and Knowledge Management, CIKM 2025, Seoul, Republic of Korea, November 10-14, 2025}},
pages = {4379--4389},
publisher = {ACM},
year = {2025},
url = {https://doi.org/10.1145/3746252.3761125},
doi = {https://doi.org/10.1145/3746252.3761125}
}