Graph Attention Network - Interactive Visualization

Graph Attention Networks extend message-passing GNNs by computing attention weights between each node and its neighbors. Instead of treating all neighbors equally, a GAT learns to attend more to influential neighbors. The attention coefficient between node i and neighbor j is computed from their concatenated feature vectors - allowing the model to learn which connections matter most for the task.

• Watch message passing: each node collects feature vectors from its neighbors and aggregates them into an updated embedding
• See attention weights visualized as edge thickness - thicker edges indicate higher attention, the model focuses on influential neighbors
• Understand multi-head attention in GATs: K parallel attention mechanisms whose outputs are concatenated or averaged
• Compare GCN (equal neighbor weighting) vs GAT (learned weights) - GAT is more expressive but more expensive
• Learn why graphs require special neural network architectures: unlike images, graphs have no fixed spatial structure or grid

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