💡 Anomaly Detection

[ICML 2025 Spotlight] Leveraging diffusion model as pseudo-anomalous graph generator for graph-level anomaly detection,
Jinyu Cai, Yunhe Zhang, Fusheng Liu, See-Kiong Ng
Proceedings of the International Conference on Machine Learning. (CCF A)

[ICML 2025] Self-discriminative modeling for anomalous graph detection,
Jinyu Cai, Yunhe Zhang, Jicong Fan
Proceedings of the International Conference on Machine Learning. (CCF A)

[ACM MM 2024] Towards effective federated graph anomaly detection via self-boosted knowledge distillation,
Jinyu Cai, Yunhe Zhang, Zhoumin Lu, Wenzhong Guo, See-Kiong Ng
Proceedings of the 32nd ACM International Conference on Multimedia, Pages 5537-5546, 2024. (CCF A) | [code]

[IJCAI 2024] LG-FGAD: An effective federated graph anomaly detection framework,
Jinyu Cai, Yunhe Zhang, Jicong Fan, See-Kiong Ng
Proceedings of the 33rd International Joint Conference on Artificial Intelligence, Pages 3760-3769, 2024. (CCF A) | [code]

[ICLR 2024] Deep orthogonal hypersphere compression for anomaly detection,
Yunhe Zhang, Yan Sun, Jinyu Cai, Jicong Fan
Proceedings of the 12th International Conference on Learning Representations, 2024. | [code]

[NeurIPS 2022] Perturbation learning based anomaly detection,
Jinyu Cai, Jicong Fan
Advances in Neural Information Processing Systems, 2022. (CCF A) | [code]