Deconstructing GNNs from Scratch
Explore the mathematics of message passing and graph convolutions — built entirely from scratch in pure PyTorch.
Part 1
The Math of Message Passing
Breaking down graph convolution, adjacency normalization, neighborhood aggregation, and the GCN spectral perspective.
Part 2
PyTorch Implementation
Building GCN and GAT layers with attention-weighted aggregation — entirely from scratch without torch_geometric.
View Code on GitHub
Part 3
Classifying Nodes in Graphs
Node classification on Zachary's Karate Club with only 4 labeled examples, and visualizing learned attention weights.