How do graph neural networks work?
Graph Neural Networks (GNNs) are a powerful machine learning approach designed to operate directly on graph-structured data. Unlike traditional neural networks that expect fixed-size inputs like images or text, GNNs can learn representations of nodes and edges by aggregating information from their neighbors. This allows them to capture the relational dependencies inherent in network data. They learn by iteratively passing messages between connected nodes, effectively learning features that depend on the local network structure and propagating information through the graph.
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