  Graph transformation that predicts graph transition from one mode to another
is an important and common problem. Despite much progress in developing
advanced graph transformation techniques in recent years, the fundamental
assumption typically required in machine-learning models that the testing and
training data preserve the same distribution does not always hold. As a result,
domain generalization graph transformation that predicts graphs not available
in the training data is under-explored, with multiple key challenges to be
addressed including (1) the extreme space complexity when training on all
input-output mode combinations, (2) difference of graph topologies between the
input and the output modes, and (3) how to generalize the model to (unseen)
target domains that are not in the training data. To fill the gap, we propose a
multi-input, multi-output, hypernetwork-based graph neural network
(MultiHyperGNN) that employs a encoder and a decoder to encode topologies of
both input and output modes and semi-supervised link prediction to enhance the
graph transformation task. Instead of training on all mode combinations,
MultiHyperGNN preserves a constant space complexity with the encoder and the
decoder produced by two novel hypernetworks. Comprehensive experiments show
that MultiHyperGNN has a superior performance than competing models in both
prediction and domain generalization tasks.
