Graph Neural Networks
Graph Neural Networks (GNNs) are a type of neural network that operate on graph-structured data. They are designed to learn representations of nodes and edges in a graph, and to use these representations to perform various tasks such as node classification, link prediction, and graph classification. GNNs typically operate by iteratively aggregating information from a node's neighbors, and updating the node's representation based on this aggregated information. This process is repeated for multiple iterations, allowing the network to capture complex relationships between nodes and edges in the graph. GNNs have been successfully applied to a wide range of domains, including social networks, bioinformatics, and recommendation systems.
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