Self-Supervised learning aims to eliminate the need for expensive annotation
in graph representation learning, where graph contrastive learning (GCL) is
trained with the self-supervision signals containing data-data pairs. These
data-data pairs are generated with augmentation employing stochastic functions
on the original graph. We argue that some features can be more critical than
others depending on the downstream task, and applying stochastic function
uniformly, will vandalize the influential features, leading to diminished
accuracy. To fix this issue, we introduce a Feature Based Adaptive Augmentation
(FebAA) approach, which identifies and preserves potentially influential
features and corrupts the remaining ones. We implement FebAA as plug and play
layer and use it with state-of-the-art Deep Graph Contrastive Learning (GRACE)
and Bootstrapped Graph Latents (BGRL). We successfully improved the accuracy of
GRACE and BGRL on eight graph representation learning's benchmark datasets.

通过应用特征基于自适应数据扩充（FebAA）方法，可以提高自监督学习模型中的准确性，这基于图形对比学习和深层图形对比学习，通过维护数据关键特征和误差特征，可以有效消除数据外推问题。