awesome-gcn

Why we included this project

Graph learning is a sprawling field, and finding the right starting code often means digging through a dozen half-finished repos. This index collects GCN and graph attention implementations and groups them by task, so knowledge graphs, text classification, question answering, and recommendation work each get their own section instead of one flat link dump. Alongside the classic TensorFlow and PyTorch code people usually run first, it includes tutorials and improved variants for anyone who wants to go beyond the baselines. The task-level structure is the real payoff: if you are reproducing the original GCN paper or hunting for a graph attention model for recommendation, you can jump straight to a relevant repo. It has not been updated since 2019, so treat it as a solid historical entry point to the field's foundational work rather than a source for the latest methods.