  To take full advantage of fast-growing unlabeled networked data, this paper
introduces a novel self-supervised strategy for graph representation learning
by exploiting natural supervision provided by the data itself. Inspired by
human social behavior, we assume that the global context of each node is
composed of all nodes in the graph since two arbitrary entities in a connected
network could interact with each other via paths of varying length. Based on
this, we investigate whether the global context can be a source of free and
effective supervisory signals for learning useful node representations.
Specifically, we randomly select pairs of nodes in a graph and train a
well-designed neural net to predict the contextual position of one node
relative to the other. Our underlying hypothesis is that the representations
learned from such within-graph context would capture the global topology of the
graph and finely characterize the similarity and differentiation between nodes,
which is conducive to various downstream learning tasks. Extensive benchmark
experiments including node classification, clustering, and link prediction
demonstrate that our approach outperforms many state-of-the-art unsupervised
methods and sometimes even exceeds the performance of supervised counterparts.
