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.

对对比度图自我监督学习中光谱信息的贡献进行讨论，发现基于光谱特征的增强方法在学习性能上表现优异，但基于边缘摄动的简单策略在性能方面更出色，且资源消耗更低。这些结论为图像自我监督学习领域带来了重要进展，可能改变对图像自我监督学习的理解和应用。