Graph Neural Networks emerged from early efforts to apply neural computation to structured, relational data. Over the past decade, the field has developed from foundational spectral methods into a broad family of approaches for learning across molecules, social networks, knowledge graphs, and other connected systems.

Early work established ways to extend convolutional ideas beyond grids and sequences, allowing models to learn from both individual entities and the relationships between them. Later architectures refined how information is passed across a graph, improving scalability, expressiveness, and performance on increasingly complex networks.

Today, GNNs sit at the intersection of graph theory and deep learning, supporting research in areas from drug discovery and recommendation systems to traffic forecasting and scientific modelling.

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