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Related Questions
- What are the key differences between graph neural networks and traditional neural networks?
- Can you explain how graph neural networks learn to represent nodes and edges in a graph?
- How do graph neural networks handle complex graph structures compared to traditional neural networks?
- What are some common applications of graph neural networks, such as social network analysis or molecule modeling?
- How do graph neural networks generalize to new nodes and edges in a graph?
- Can you discuss the trade-offs between the expressive power of graph neural networks and their computational complexity?
- How do graph neural networks compare to other methods for processing graph-structured data, such as matrix factorization?
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