This repository aggregates academic resources on graph neural networks, organizing papers by architecture type (recurrent, convolutional, autoencoders, spatial-temporal) and application domain (computer vision, NLP, healthcare, chemistry, physics). It serves as a reference for researchers and practitioners studying deep learning on graph-structured data.