UFO: A Unified Flow-Oriented Framework for Robust Continual Graph Learning

ArXi:2605.09862v1 Announce Type: cross Graph learning research has increasingly shifted toward continual graph learning (CGL), which better reflects real-world scenarios where graphs evolve over time. However, existing CGL methods largely assume clean supervision and overlook a critical challenge: the newly arriving portions of the graph are often noisy, due to annotation errors or adversarial corruption. This mismatch limits their applicability in practice.