\section{Introduction}\label{sec:intro}

State-of-the-art graph self-supervised learning methods (SSL) belong to two catagories, contrast-based methods and reconstruction-based methods. 

Don't call it prediction. Call it Proximity Divergence Minimization.
Proposal and target distribution

This measurement will naturally incorporate multiple connection between two nodes of interest; and it also considers the "quality" for each individual connection (punishing longer connection, higher degree nodes along the connection etc).

connection with knowledge distillation

Rich knowledge is contained within the structure of graph. Supervision signals can be found in the relationship between nodes, the problem is how to take advantage of relationship between nodes to construct meaningful self-supervised learning objective for graph data.

We take advantage of higher-order node proximity measures. 