Node classification helps link prediction consistently, but link prediction rarely helps node classification unless the graph structure is very predictable—use graph homophily as a signal for whether to share model weights across these tasks.
This paper investigates how knowledge transfers between node classification and link prediction tasks on the same graph. The authors establish a standardized evaluation protocol to eliminate unfair comparisons, then show that transfer works well in one direction (NC→LP) but is unreliable in the other (LP→NC).