  Generative graph self-supervised learning (SSL) aims to learn node
representations by reconstructing the input graph data. However, most existing
methods focus on unsupervised learning tasks only and very few work has shown
its superiority over the state-of-the-art graph contrastive learning (GCL)
models, especially on the classification task. While a very recent model has
been proposed to bridge the gap, its performance on unsupervised learning tasks
is still unknown. In this paper, to comprehensively enhance the performance of
generative graph SSL against other GCL models on both unsupervised and
supervised learning tasks, we propose the SeeGera model, which is based on the
family of self-supervised variational graph auto-encoder (VGAE). Specifically,
SeeGera adopts the semi-implicit variational inference framework, a
hierarchical variational framework, and mainly focuses on feature
reconstruction and structure/feature masking. On the one hand, SeeGera
co-embeds both nodes and features in the encoder and reconstructs both links
and features in the decoder. Since feature embeddings contain rich semantic
information on features, they can be combined with node embeddings to provide
fine-grained knowledge for feature reconstruction. On the other hand, SeeGera
adds an additional layer for structure/feature masking to the hierarchical
variational framework, which boosts the model generalizability. We conduct
extensive experiments comparing SeeGera with 9 other state-of-the-art
competitors. Our results show that SeeGera can compare favorably against other
state-of-the-art GCL methods in a variety of unsupervised and supervised
learning tasks.
