Graph self-supervised learning (SSL) has been vastly employed to learn
representations from unlabeled graphs. Existing methods can be roughly divided
into predictive learning and contrastive learning, where the latter one
attracts more research attention with better empirical performance. We argue
that, however, predictive models weaponed with powerful decoder could achieve
comparable or even better representation power than contrastive models. In this
work, we propose a Wiener Graph Deconvolutional Network (WGDN), an
augmentation-adaptive decoder empowered by graph wiener filter to perform
information reconstruction. Theoretical analysis proves the superior
reconstruction ability of graph wiener filter. Extensive experimental results
on various datasets demonstrate the effectiveness of our approach.

本文提出了使用改进的信息重构器 ——WGDN，它采用基于图维纳滤波器的增强型解码器，并分析理论证明了图维纳滤波器的出色重构能力。实验结果表明该模型比基于对比学习的模型更有效，同时也证明了具备强大解码能力的预测模型可以获得可比甚至更好的表示能力。