Continual Graph Learning for Fraud Detection Under Adversarial Drift
Financial transaction networks face a persistent threat from strategic adversarial drift, a phenomenon where sophisticated threat actors manipulate graph structures to bypass conventional fraud detection systems. According to researchers, traditional temporal graph neural networks frequently fail in these scenarios because they discard historical patterns during retraining and struggle to generalize against novel structural perturbations. Understanding … Read more