Lesson 315
Graph Neural Networks
Message Passing on Graphs
1:00How graph neural networks learn by passing messages along edges — the message-aggregate-update template, the GCN layer, receptive-field growth, oversmoothing, and where GNNs are used in practice.
By the end, you can
- Explain why ordinary neural networks cannot directly process graph-structured data and what permutation invariance requires.
- Describe the three sub-steps of one message-passing round: message, aggregate, update.
- Justify why aggregation must use sum, mean, or max rather than concatenation.
- Trace the GCN layer formula, identifying the role of self-loops, symmetric normalization, and the shared weight matrix W.
- Predict a node's receptive field size after L GNN layers.
- Define oversmoothing and state the practical depth limit it imposes.
- Classify a graph learning task as node-level, edge-level, or graph-level and give a real-world example of each.
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