  Graph representation learning has attracted lots of attention recently.
Existing graph neural networks fed with the complete graph data are not
scalable due to limited computation and memory costs. Thus, it remains a great
challenge to capture rich information in large-scale graph data. Besides, these
methods mainly focus on supervised learning and highly depend on node label
information, which is expensive to obtain in the real world. As to unsupervised
network embedding approaches, they overemphasize node proximity instead, whose
learned representations can hardly be used in downstream application tasks
directly. In recent years, emerging self-supervised learning provides a
potential solution to address the aforementioned problems. However, existing
self-supervised works also operate on the complete graph data and are biased to
fit either global or very local (1-hop neighborhood) graph structures in
defining the mutual information based loss terms.
  In this paper, a novel self-supervised representation learning method via
Subgraph Contrast, namely \textsc{Subg-Con}, is proposed by utilizing the
strong correlation between central nodes and their sampled subgraphs to capture
regional structure information. Instead of learning on the complete input graph
data, with a novel data augmentation strategy, \textsc{Subg-Con} learns node
representations through a contrastive loss defined based on subgraphs sampled
from the original graph instead. Compared with existing graph representation
learning approaches, \textsc{Subg-Con} has prominent performance advantages in
weaker supervision requirements, model learning scalability, and
parallelization. Extensive experiments verify both the effectiveness and the
efficiency of our work compared with both classic and state-of-the-art graph
representation learning approaches on multiple real-world large-scale benchmark
datasets from different domains.
