A new self-supervised task on graphs: Geodesic distance prediction

The heavy dependence on human-curated labels makes supervised algorithms hit a bottleneck in real applications. Fortunately, a more data-efficient learning paradigm—self-supervised learning (SSL) disrupts this dilemma. By creating pretext tasks, self-supervised algorithms learn from the data itself without external supervision. Until recently, SSL has yielded immense success in the image domain, but its research in graph mining has received relatively little scrutiny. In fact, rich topological links and affiliated attributes promise more potential for self-supervised graph learning. Thus, in this paper, we propose a new pretext task called geodesic distance prediction to guide node representation learning. Specifically, this task demands neural nets to learn to infer the geodesic distance of one node relative to the other. Our underlying hypothesis is that doing well on reasoning about the pairwise distance requires the model to extract relation rules between topological distances and attributes. In this way, the complex correlation between nodes can be measured with a simple distance scale. Experiments demonstrate that our S²GRL achieves competitive or better performance than many state-of-the-art self-supervised methods. A case study on distance inference shows that S²GRL can effectively evaluate the risk of business transactions or make recommendations from a topological view.