Graph Convolutional Networks (GCN)
Message passing framework, GCNConv, node feature aggregation, PyTorch Geometric setup.
From CNNs to GCNs
CNNs exploit grid structure in images. A Graph Convolutional Network (GCN) generalizes convolution to irregular graphs, letting each node update its representation using information from its neighbors. This handles data with no fixed grid.
Message Passing
GCNs follow the message passing framework: at each layer every node (1) gathers messages from its neighbors, (2) aggregates them, and (3) updates its own embedding. Stacking layers lets information flow across the graph.
All lessons in this course
- Graph Theory for Machine Learning
- Graph Convolutional Networks (GCN)
- Node Classification with GNN
- Link Prediction and Graph Classification