Graph Convolutional Network by Hand: 12-Step Walkthrough
ProfTomYeh · x · 2026-08-23
Prof. Tom Yeh provides a 12-step hand-calculated walkthrough of Graph Convolutional Networks (GCNs). The demo covers adjacency matrices, self-loops, message passing, neighbor pooling, and classification. The key takeaway is that a GCN layer consists of pooling neighbors via an adjacency matrix and transforming nodes via an MLP, closely resembling a Transformer layer where the attention matrix is replaced by a fixed adjacency matrix.
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