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.
Out-of- distribution detection on graphs: A survey.arXiv preprint arXiv:2502.08105
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FedLAB organizes multimodal graph knowledge into typed hierarchical codebooks for modality evidence, node semantics, and topology context via federated semantic barycenter pre-training, improving performance by up to 7.53% on benchmarks while enabling semantic traceability.
CORE decouples site confounders in fMRI networks, profiles transient dynamics on a population scaffold using line graphs, and applies subject-adaptive gating to achieve up to 6.7% better cross-site generalization on ABIDE, REST-meta-MDD, SRPBS, and ABCD datasets.
citing papers explorer
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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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FedLAB: Traceable Semantic Codebooks for Federated Multimodal Graph Foundation Learning
FedLAB organizes multimodal graph knowledge into typed hierarchical codebooks for modality evidence, node semantics, and topology context via federated semantic barycenter pre-training, improving performance by up to 7.53% on benchmarks while enabling semantic traceability.
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When Brain Networks Travel: Learning Beyond Site
CORE decouples site confounders in fMRI networks, profiles transient dynamics on a population scaffold using line graphs, and applies subject-adaptive gating to achieve up to 6.7% better cross-site generalization on ABIDE, REST-meta-MDD, SRPBS, and ABCD datasets.