  Graph neural networks (GNNs) are powerful black-box models which have shown
impressive empirical performance. However, without any form of uncertainty
quantification, it can be difficult to trust such models in high-risk
scenarios. Conformal prediction aims to address this problem, however, an
assumption of exchangeability is required for its validity which has limited
its applicability to static graphs and transductive regimes. We propose to use
unfolding, which allows any existing static GNN to output a dynamic graph
embedding with exchangeability properties. Using this, we extend the validity
of conformal prediction to dynamic GNNs in both transductive and semi-inductive
regimes. We provide a theoretical guarantee of valid conformal prediction in
these cases and demonstrate the empirical validity, as well as the performance
gains, of unfolded GNNs against standard GNN architectures on both simulated
and real datasets.
