通过子图高斯嵌入提升图对比学习的鲁棒性
Variational Graph Contrastive Learning
- 用子图高斯嵌入模块将子图映射到结构化空间
- 引入最优传输距离，增强对比学习的稳定性
- 适合研究自监督图表示学习的学者参考
图表示学习（GRL）旨在将高维图数据编码为低维向量。自监督学习（SSL）因无需人工标注而被广泛采用。本文提出一种新的子图高斯嵌入对比方法（SGEC），引入子图高斯嵌入模块，自适应地将子图映射至结构化高斯空间，既保留图特征又控制生成子图的分布。采用最优传输距离（如Wasserstein和Gromov-Wasserstein）有效衡量子图间相似性，提升对比学习鲁棒性。在多个基准测试中，SGEC表现优于或媲美现有先进方法。研究结果为自监督图学习中的对比对分布设计提供了新思路。
原文摘要 · Abstract (English)
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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