Feature-Context Consistency: Unsupervised Adversarial Detection with Drift Stability on Attributed Graphs.

arXiv:2610.16694v1 Announce Type: cross Abstract: 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 reac