Graph Encoder

Graph encoders are machine learning models designed to learn meaningful representations of graph-structured data, aiming to capture both local and global structural information for various downstream tasks like node classification, link prediction, and graph classification. Current research emphasizes developing more efficient and robust encoders, often employing architectures like graph neural networks (GNNs) and transformers, with a focus on improving scalability for large graphs and incorporating self-supervised learning techniques such as contrastive learning to leverage unlabeled data. These advancements are significant because effective graph encoders are crucial for analyzing complex relational data across diverse fields, including social networks, bioinformatics, and materials science.