Graph Attention Networks (GAT)
Adaptive attention-based aggregation for graph neural networks - multi-head attention, learned weights, and interpretable graph learning
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Graph Attention Networks (GAT)
Adaptive attention-based aggregation for graph neural networks
Click nodes to visualize attention weights
Attention Mechanism: GAT learns to assign different importance weights to different neighbors, allowing the model to focus on the most relevant connections.
Overview
Graph Attention Networks (GAT) introduce attention mechanisms to graph neural networks, allowing nodes to adaptively weight their neighbors' contributions based on learned attention coefficients. Unlike GCNs with fixed weights, GATs learn which neighbors are most relevant for each node.
Key Concepts
Attention Mechanism
- Query-Key-Value: Transform node features into queries and keys
- Attention Scores: Compute compatibility between node pairs
- Softmax Normalization: Convert scores to probabilities
- Weighted Aggregation: Combine neighbor features with attention weights
Multi-Head Attention
- Parallel Attention: Multiple attention heads learn different relationships
- Feature Diversity: Each head focuses on different aspects
- Concatenation/Average: Combine outputs from all heads
- Improved Stability: More robust learning through ensemble
Advantages Over GCN
- Adaptive Weights: Learn importance of each neighbor
- Interpretability: Visualize attention patterns
- Inductive Learning: Generalize to unseen graphs
- Parallelizable: Efficient computation across edges
Applications
- Social network analysis with varying relationship strengths
- Molecular property prediction with chemical bond attention
- Knowledge graph reasoning with relation-aware attention
- Traffic prediction with dynamic road importance
Implementation Tips
- Use LeakyReLU for attention coefficient computation
- Apply dropout to attention weights for regularization
- Initialize attention parameters carefully
- Monitor attention entropy to detect collapse