Graph Masked

Graph masked autoencoders (GMAEs) are a self-supervised learning approach for graph-structured data aiming to learn robust and informative node and graph representations from unlabeled data. Current research focuses on improving masking strategies (e.g., structure-guided, adaptive masking) and enhancing model architectures, including hierarchical and transformer-based GMAEs, to better capture complex graph structures and node importance. These advancements are significant because effective graph representation learning is crucial for various applications, such as spatial transcriptomics analysis, molecular property prediction, and recommendation systems, where labeled data is often scarce.