Awesome-Graph-Neural-Networks

Why we included this project

Graph neural networks have a scattered literature, and this list does the sorting for you. It groups papers by model family, covering recurrent, convolutional, autoencoder, and spatial-temporal variants, then organizes them by application area such as recommender systems, healthcare, chemistry, and computer vision. That structure makes it easy to find the work closest to your own problem without wading through years of arXiv postings. The survey section at the top is a good place to start if you want a map of the field before reading primary papers. Since this is a curated index rather than a codebase, its real value is the coverage and organization it brings to literature review and background research.