\section{Conclusion}

% We propose a novel graph SSL approach, CURSIVE, that uses a natural corruption-free learning objective and 
We propose \method, a novel graph SSL framework that is corruption-free and uses a single view. By avoiding corruption techniques employed by prior SSL method, we achieve a natural SSL objective to attain significantly better accuracy on a variety of datasets with minimal tuning efforts. Without the computation of multiple views, our method is more memory-efficient and scalable than prior approaches and able to scale to large graphs that are difficult for existing methods. Through extensive experiments, we demonstrate the advantage of \method over existing SSL methods with significant improvements in accuracy on popular graph benchmarks. Furthermore, our SSL approach is able to surpass or be competitive with the accuracy of fully supervised training on large-scale datasets, which is a crucial step towards eliminating the need for costly labels in graph learning.