Can you explain what graph neural networks are?
Graph Neural Networks (GNNs) are a type of neural network designed to process data structured as graphs. They capture relationships between nodes.
How do GNNs differ from traditional neural networks?
Unlike traditional neural networks that work on grid-like data, GNNs operate on graph structures, allowing them to model complex relationships.
What are some applications of graph neural networks?
GNNs are used in various fields, including social network analysis, recommendation systems, and drug discovery, to analyze relationships.
Can you give an example of a real-world use case?
Sure! In drug discovery, GNNs can predict molecular interactions by modeling molecules as graphs, helping to identify potential new drugs.
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