  Graph Neural Networks have shown tremendous potential on dealing with garph
data and achieved outstanding results in recent years. In some research areas,
labelling data are hard to obtain for technical reasons, which necessitates the
study of unsupervised and semi-superivsed learning on graphs. Therefore,
whether the learned representations can capture the intrinsic feature of the
original graphs will be the issue in this area. In this paper, we introduce a
self-supervised learning method to enhance the representations of graph-level
learned by Graph Neural Networks. To fully capture the original attributes of
the graph, we use three information aggregators: attribute-conv, layer-conv and
subgraph-conv to gather information from different aspects. To get a
comprehensive understanding of the graph structure, we propose an
ensemble-learning like subgraph method. And to achieve efficient and effective
contrasive learning, a Head-Tail contrastive samples construction method is
proposed to provide more abundant negative samples. By virtue of all proposed
components which can be generalized to any Graph Neural Networks, in
unsupervised case, we achieve new state of the art results in several
benchmarks. We also evaluate our model on semi-supervised learning tasks and
make a fair comparison to state of the art semi-supervised methods.
