PTCL targets dynamic node classification when only final-timestamp labels are available. A decoupled architecture learns time-aware representations while producing pseudo-labels aligned with the observed final labels, and a temporal curriculum weights pseudo-labels according to their reliability over time. The work also introduces CoOAG and the FLiD evaluation framework for label-limited dynamic graphs.
Temporal curriculum learning makes final-timestamp labels useful across the full evolution of a dynamic graph.