  Graph neural networks have been used for a variety of learning tasks, such as
link prediction, node classification, and node clustering. Among them, link
prediction is a relatively under-studied graph learning task, with current
state-of-the-art models based on one- or two-layer of shallow graph
auto-encoder (GAE) architectures. In this paper, we focus on addressing a
limitation of current methods for link prediction, which can only use shallow
GAEs and variational GAEs, and creating effective methods to deepen
(variational) GAE architectures to achieve stable and competitive performance.
Our proposed methods innovatively incorporate standard auto-encoders (AEs) into
the architectures of GAEs, where standard AEs are leveraged to learn essential,
low-dimensional representations via seamlessly integrating the adjacency
information and node features, while GAEs further build multi-scaled
low-dimensional representations via residual connections to learn a compact
overall embedding for link prediction. Empirically, extensive experiments on
various benchmarking datasets verify the effectiveness of our methods and
demonstrate the competitive performance of our deepened graph models for link
prediction. Theoretically, we prove that our deep extensions inclusively
express multiple polynomial filters with different orders.
