  Recently, Graph Neural Networks (GNNs) have gained popularity in a variety of
real-world scenarios. Despite the great success, the architecture design of
GNNs heavily relies on manual labor. Thus, automated graph neural network
(AutoGNN) has attracted interest and attention from the research community,
which makes significant performance improvements in recent years. However,
existing AutoGNN works mainly adopt an implicit way to model and leverage the
link information in the graphs, which is not well regularized to the link
prediction task on graphs, and limits the performance of AutoGNN for other
graph tasks. In this paper, we present a novel AutoGNN work that explicitly
models the link information, abbreviated to AutoGEL. In such a way, AutoGEL can
handle the link prediction task and improve the performance of AutoGNNs on the
node classification and graph classification task. Specifically, AutoGEL
proposes a novel search space containing various design dimensions at both
intra-layer and inter-layer designs and adopts a more robust differentiable
search algorithm to further improve efficiency and effectiveness. Experimental
results on benchmark data sets demonstrate the superiority of AutoGEL on
several tasks.
