CAMERA is an ego-decoupled mixture-of-experts model with context-informed gating and one-class objectives for unsupervised fraud detection in text-attributed graphs facing semantic camouflage.
Bisecle: Binding and separa- tion in continual learning for video language understand- ing.Advances in Neural Information Processing Systems, 38:33752–33782
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FedCIGAR improves federated graph anomaly detection via normal-graph reconstruction, client node gating, and server sliding-window clustering, claiming better performance than prior methods under data heterogeneity.
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CAMERA: Adapting to Semantic Camouflage in Unsupervised Text-Attributed Graph Fraud Detection
CAMERA is an ego-decoupled mixture-of-experts model with context-informed gating and one-class objectives for unsupervised fraud detection in text-attributed graphs facing semantic camouflage.
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FedCIGAR: A Personalized Reconstruction Approach for Federated Graph-level Anomaly Detection
FedCIGAR improves federated graph anomaly detection via normal-graph reconstruction, client node gating, and server sliding-window clustering, claiming better performance than prior methods under data heterogeneity.