Graph Neural Networks (GNNs) are a powerful alternative for modeling relational data in financial networks and protein structures, which are complex and not well-represented in Euclidean space. GNNs capture relationships between entities more effectively than traditional deep learning architectures, making them crucial for tasks such as fraud detection and protein function prediction. GNNs use message-passing frameworks to update node embeddings based on aggregated features from neighboring nodes, ultimately improving detection and prediction accuracy in various applications.
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