  Conformal prediction (CP) transforms any model's output into prediction sets
guaranteed to include (cover) the true label. CP requires exchangeability, a
relaxation of the i.i.d. assumption, to obtain a valid distribution-free
coverage guarantee. This makes it directly applicable to transductive
node-classification. However, conventional CP cannot be applied in inductive
settings due to the implicit shift in the (calibration) scores caused by
message passing with the new nodes. We fix this issue for both cases of node
and edge-exchangeable graphs, recovering the standard coverage guarantee
without sacrificing statistical efficiency. We further prove that the guarantee
holds independently of the prediction time, e.g. upon arrival of a new
node/edge or at any subsequent moment.
