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Feature-Context Consistency: Unsupervised Adversarial Detection with Drift Stability on Attributed Graphs
Adversarial detection on evolving attributed graphs faces two challenges: fraud labels arrive late or not at all, and natural distributional drift erodes any trained decision boundary. Modern detectors can reach strong detection rates when labels are available and the distribution is stationary; sus
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