Graph autoencoders (GAEs) are self-supervised learning models that can learn meaningful representations of graph-structured data by reconstructing the input graph from a low-dimensional latent space. Over the past few years, GAEs have gained significant attention in academia and industry. In particular, the recent advent of GAEs with masked autoencoding schemes marks a significant advancement in graph self-supervised learning research. While numerous GAEs have been proposed, the underlying mechanisms of GAEs are not well understood, and a comprehensive benchmark for GAEs is still lacking. In this work, we bridge the gap between GAEs and contrastive learning by establishing conceptual and methodological connections. We revisit the GAEs studied in previous works and demonstrate how contrastive learning principles can be applied to GAEs. Motivated by these insights, we introduce lrGAE (left-right GAE), a general and powerful GAE framework that leverages contrastive learning principles to learn meaningful representations. Our proposed lrGAE not only facilitates a deeper understanding of GAEs but also sets a new benchmark for GAEs across diverse graph-based learning tasks. The source code for lrGAE, including the baselines and all the code for reproducing the results, is publicly available at https://github.com/EdisonLeeeee/lrGAE.

翻译：图自编码器（GAE）是一种自监督学习模型，它通过从低维潜在空间重建输入图来学习图结构数据的有意义表示。在过去几年中，GAE 在学术界和工业界获得了广泛关注。特别是，最近采用掩码自编码方案的 GAE 的出现，标志着图自监督学习研究的重要进展。尽管已有大量 GAE 模型被提出，但 GAE 的内在机制尚未得到充分理解，并且仍缺乏一个全面的 GAE 基准。在本工作中，我们通过建立概念和方法上的联系，弥合了 GAE 与对比学习之间的鸿沟。我们重新审视了先前工作中研究的 GAE，并展示了如何将对比学习原理应用于 GAE。受这些见解的启发，我们提出了 lrGAE（左-右 GAE），这是一个通用且强大的 GAE 框架，它利用对比学习原理来学习有意义的表示。我们提出的 lrGAE 不仅有助于更深入地理解 GAE，还为各种基于图的学习任务中的 GAE 设立了新的基准。lrGAE 的源代码，包括基线模型和用于复现结果的所有代码，已在 https://github.com/EdisonLeeeee/lrGAE 公开提供。