Graph representation learning (GRL) is a fundamental task in machine learning, aiming to encode high-dimensional graph-structured data into low-dimensional vectors. Self-supervised learning (SSL) methods are widely used in GRL because they can avoid expensive human annotation. In this work, we propose a novel Subgraph Gaussian Embedding Contrast (SGEC) method. Our approach introduces a subgraph Gaussian embedding module, which adaptively maps subgraphs to a structured Gaussian space, ensuring the preservation of graph characteristics while controlling the distribution of generated subgraphs. We employ optimal transport distances, including Wasserstein and Gromov-Wasserstein distances, to effectively measure the similarity between subgraphs, enhancing the robustness of the contrastive learning process. Extensive experiments across multiple benchmarks demonstrate that SGEC outperforms or presents competitive performance against state-of-the-art approaches. Our findings provide insights into the design of SSL methods for GRL, emphasizing the importance of the distribution of the generated contrastive pairs.

翻译：图表示学习是机器学习中的一项基础任务，旨在将高维图结构数据编码为低维向量。自监督学习方法因其能够避免昂贵的人工标注而在图表示学习中得到广泛应用。本文提出了一种新颖的子图高斯嵌入对比方法。我们的方法引入了一个子图高斯嵌入模块，该模块自适应地将子图映射到结构化高斯空间，在控制生成子图分布的同时确保图特征的保留。我们采用最优传输距离，包括Wasserstein距离和Gromov-Wasserstein距离，来有效度量子图之间的相似性，从而增强对比学习过程的鲁棒性。在多个基准数据集上的大量实验表明，SGEC方法优于或展现出与最先进方法相竞争的性能。我们的研究结果为图表示学习中自监督方法的设计提供了见解，强调了生成对比对分布的重要性。