  Self-supervised Learning (SSL) aims at learning representations of objects
without relying on manual labeling. Recently, a number of SSL methods for graph
representation learning have achieved performance comparable to SOTA
semi-supervised GNNs. A Siamese network, which relies on data augmentation, is
the popular architecture used in these methods. However, these methods rely on
heuristically crafted data augmentation techniques. Furthermore, they use
either contrastive terms or other tricks (e.g., asymmetry) to avoid trivial
solutions that can occur in Siamese networks. In this study, we propose,
GraphSurgeon, a novel SSL method for GNNs with the following features. First,
instead of heuristics we propose a learnable data augmentation method that is
jointly learned with the embeddings by leveraging the inherent signal encoded
in the graph. In addition, we take advantage of the flexibility of the
learnable data augmentation and introduce a new strategy that augments in the
embedding space, called post augmentation. This strategy has a significantly
lower memory overhead and run-time cost. Second, as it is difficult to sample
truly contrastive terms, we avoid explicit negative sampling. Third, instead of
relying on engineering tricks, we use a scalable constrained optimization
objective motivated by Laplacian Eigenmaps to avoid trivial solutions. To
validate the practical use of GraphSurgeon, we perform empirical evaluation
using 14 public datasets across a number of domains and ranging from small to
large scale graphs with hundreds of millions of edges. Our finding shows that
GraphSurgeon is comparable to six SOTA semi-supervised and on par with five
SOTA self-supervised baselines in node classification tasks. The source code is
available at https://github.com/zekarias-tilahun/graph-surgeon.
