Variational Graph Contrastive Learning
This work addresses the problem of learning graph representations without human annotation for machine learning applications, but it appears incremental as it builds on existing self-supervised and contrastive learning techniques.
The paper tackles graph representation learning by proposing a novel Subgraph Gaussian Embedding Contrast (SGEC) method, which uses subgraph Gaussian embeddings and optimal transport distances to improve contrastive learning, achieving competitive or superior performance on multiple benchmarks.
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.