  The recent surge in contrast-based graph self-supervised learning has
prominently featured an intensified exploration of spectral cues. However, an
intriguing paradox emerges, as methods grounded in seemingly conflicting
assumptions or heuristic approaches regarding the spectral domain demonstrate
notable enhancements in learning performance. This paradox prompts a critical
inquiry into the genuine contribution of spectral information to contrast-based
graph self-supervised learning. This study undertakes an extensive
investigation into this inquiry, conducting a thorough study of the
relationship between spectral characteristics and the learning outcomes of
contemporary methodologies. Based on this analysis, we claim that the
effectiveness and significance of spectral information need to be questioned.
Instead, we revisit simple edge perturbation: random edge dropping designed for
node-level self-supervised learning and random edge adding intended for
graph-level self-supervised learning. Compelling evidence is presented that
these simple yet effective strategies consistently yield superior performance
while demanding significantly fewer computational resources compared to all
prior spectral augmentation methods. The proposed insights represent a
significant leap forward in the field, potentially reshaping the understanding
and implementation of graph self-supervised learning.
