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。

基于对比度的图谱自监督学习中光谱提示是否重要？

Do spectral cues matter in contrast-based graph self-supervised  learning?

Edge perturbation is a basic method to modify graph structures. It can be
categorized into two veins based on their effects on the performance of graph
neural networks (GNNs), i.e., graph data augmentation and attack. Surprisingly,
both veins of edge perturbation methods employ the same operations, yet yield
opposite effects on GNNs' accuracy. A distinct boundary between these methods
in using edge perturbation has never been clearly defined. Consequently,
inappropriate perturbations may lead to undesirable outcomes, necessitating
precise adjustments to achieve desired effects. Therefore, questions of ``why
edge perturbation has a two-faced effect?'' and ``what makes edge perturbation
flexible and effective?'' still remain unanswered.
In this paper, we will answer these questions by proposing a unified
formulation and establishing a clear boundary between two categories of edge
perturbation methods. Specifically, we conduct experiments to elucidate the
differences and similarities between these methods and theoretically unify the
workflow of these methods by casting it to one optimization problem. Then, we
devise Edge Priority Detector (EPD) to generate a novel priority metric,
bridging these methods up in the workflow. Experiments show that EPD can make
augmentation or attack flexibly and achieve comparable or superior performance
to other counterparts with less time overhead.

该研究提出了边界扰动方法的两个类别，并通过实验和理论的统一构建了边界扰动方法的工作流程。同时，引入了边界优先检测器（EPD），通过较少的时间开销实现了灵活的增强或攻击，并获得了可比或更好的性能。