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.

通过使用基于超网络的图神经网络和半监督链接预测，我们提出了一种多输入多输出的方法，用于预测不在训练数据中的图，并展示了该方法在预测和领域泛化任务上的卓越性能。