变分图对比学习
Variational Graph Contrastive Learning
AI总结：
提出子图高斯嵌入对比方法（SGEC），通过将子图映射到高斯空间并利用最优传输距离度量相似度，有效提升了图表示学习中的自监督对比学习性能。
摘要：
图表示学习（GRL）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（SSL）方法因能避免昂贵的人工标注而在GRL中被广泛使用。在本研究中，我们提出了一种新颖的子图高斯嵌入对比（SGEC）方法。该方法引入了子图高斯嵌入模块，能自适应地将子图映射到结构化高斯空间，在确保保留图特征的同时控制生成子图的分布。我们采用最优传输距离，包括Wasserstein和Gromov-Wasserstein距离，来有效度量子图间的相似度，增强了对比学习过程的鲁棒性。在多个基准上的大量实验表明，SGEC优于或表现出与最先进方法相媲美的性能。我们的研究结果为GRL的SSL方法设计提供了见解，强调了生成对比对分布的重要性。
英文摘要：
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.
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