  Graph representation learning is an important task with applications in
various areas such as online social networks, e-commerce networks, WWW, and
semantic webs. For unsupervised graph representation learning, many algorithms
such as Node2Vec and Graph-SAGE make use of "negative sampling" and/or noise
contrastive estimation loss. This bears similar ideas to contrastive learning,
which "contrasts" the node representation similarities of semantically similar
(positive) pairs against those of negative pairs. However, despite the success
of contrastive learning, we found that directly applying this technique to
graph representation learning models (e.g., graph convolutional networks) does
not always work. We theoretically analyze the generalization performance and
propose a light-weight regularization term that avoids the high scales of node
representations' norms and the high variance among them to improve the
generalization performance. Our experimental results further validate that this
regularization term significantly improves the representation quality across
different node similarity definitions and outperforms the state-of-the-art
methods.
