PaperNova
Graph Learning
2016
INTERMEDIATE

Semi-Supervised Classification with Graph Convolutional Networks

Thomas N. Kipf, Max Welling · 2016

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

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What you'll get

  • Outline: a plain-English breakdown of the paper's core idea, prerequisites, and the concepts you'll need to implement it.
  • Exercises: five to ten hands-on tasks, each with a concept card, a prompt, a starter code stub, and a collapsible reference solution.
  • Runnable notebook: a single .ipynb you can download and open in Jupyter or VS Code to work through every exercise.
  • Extensions: suggested follow-up experiments so you don't stop at a faithful reimplementation.