Node Embeddings

Node embeddings represent graph nodes as low-dimensional vectors, aiming to capture both node features and structural context within the network. Current research focuses on improving embedding quality through advanced architectures like graph autoencoders and graph neural networks, often incorporating contrastive learning and self-supervised techniques to address challenges such as over-smoothing and heterophily. These advancements enhance performance in downstream tasks like node classification and link prediction, impacting fields ranging from social network analysis to drug discovery by providing more accurate and efficient representations of complex relational data. Furthermore, there's a growing emphasis on improving the interpretability and explainability of these embeddings.