Self-supervised Learning (SSL) has emerged as a powerful technique in pre-training deep learning models without relying on expensive annotated labels, instead leveraging embedded signals in unlabeled data. While SSL has shown remarkable success in computer vision tasks through intuitive data augmentation, its application to graph-structured data poses challenges due to the semantic-altering and counter-intuitive nature of graph augmentations. Addressing this limitation, this paper introduces a novel non-contrastive SSL approach to Explicitly Generate a compositional Relation Graph (ExGRG) instead of relying solely on the conventional augmentation-based implicit relation graph. ExGRG offers a framework for incorporating prior domain knowledge and online extracted information into the SSL invariance objective, drawing inspiration from the Laplacian Eigenmap and Expectation-Maximization (EM). Employing an EM perspective on SSL, our E-step involves relation graph generation to identify candidates to guide the SSL invariance objective, and M-step updates the model parameters by integrating the derived relational information. Extensive experimentation on diverse node classification datasets demonstrates the superiority of our method over state-of-the-art techniques, affirming ExGRG as an effective adoption of SSL for graph representation learning.

翻译：自监督学习（Self-supervised Learning, SSL）已成为一种强大的技术，它无需依赖昂贵的标注标签，而是利用未标记数据中嵌入的信号来预训练深度学习模型。尽管SSL通过直观的数据增强在计算机视觉任务中取得了显著成功，但由于图增强具有改变语义和反直觉的特性，其在图结构数据上的应用面临挑战。针对这一局限，本文提出了一种新颖的非对比式SSL方法，以显式生成组合关系图（Explicitly-Generated Relation Graph, ExGRG），而非仅仅依赖传统的基于增强的隐式关系图。ExGRG提供了一个框架，可将先验领域知识和在线提取的信息整合到SSL的不变性目标中，其灵感来源于拉普拉斯特征映射和期望最大化算法。采用SSL的EM视角，我们的E步涉及关系图生成，以识别用于指导SSL不变性目标的候选关系；M步则通过整合推导出的关系信息来更新模型参数。在多样化的节点分类数据集上进行的大量实验表明，我们的方法优于现有最先进技术，证实了ExGRG是图表示学习中一种有效的SSL应用方案。