Practice · Foundational papers

Implement classic papers from scratch

Pick any paper below and PaperNova generates a guided workbook: an outline, exercise-wise explanations from beginner to advanced, and a downloadable Jupyter notebook you can run locally.

Go to workbooks →How the workbook works

Graph Learning

2016

INTERMEDIATE

Semi-Supervised Classification with Graph Convolutional Networks

Thomas N. Kipf, Max Welling

GCN. A first-order approximation of spectral graph convolutions that made graph neural networks simple, fast, and widely applicable.

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Why implement classic papers?

Reading a paper and implementing it are two very different skills. PaperNova's workbook tool bridges that gap: Gemini turns the paper into a sequence of small, self-contained exercises — from a warm-up reimplementation of the core idea up to advanced extensions — then assembles them into a Jupyter notebook you can run, edit and extend.

Your guided practice workbooks live in your dashboard.