Graph Decoder

Graph decoders are neural network components that reconstruct graph structures from encoded representations, aiming to improve information extraction, clustering, and other graph-related tasks. Current research focuses on developing efficient and accurate decoders, including non-autoregressive models for faster inference and those incorporating structural information theory for improved clustering. These advancements are impacting diverse fields, from handwritten mathematical expression recognition and retrosynthesis prediction to enhancing the privacy and interpretability of graph neural networks. The improved efficiency and accuracy of graph decoders are driving progress in various applications that rely on graph-structured data.