W S = Freeze(W Sgcn , . . .) (Optionally freeze selected components, e.g. gcn layers) (17) fT0 := W S (Assign weights of fS to a blank model fT0 in domain T ) where fT0 is fine-tuned into fT . As a baseline, we define a simple equivalent to the neural network in Smith et al., where we pre-train and fine-tune a supervised VGAE model with the sum readout and without any frozen layers. This is justified by GNNs having a small number of layers to avoid well-known problems such as oversmoothing. As such, the entire VGAE is fine-tuned and the strategy is termed Ψ (Tune VGAE) :