Glossary Library

Technical GlossaryDeep Learning

Graph Convolutional Network

A foundational GNN architecture that learns representations over graphs by using neighborhood information.

A Graph Convolutional Network adapts the idea of convolution from images to graph structure through node-neighborhood relationships. Each node learns a richer representation by aggregating information from its neighbors. It has important applications in social networks, molecular structures, knowledge graphs, and recommendation systems. Its core strength is that it directly incorporates relational structure into the learning process.

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Explore these concepts to continue your artificial intelligence journey.

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Representation Learning

An approach in which informative, discriminative, and task-relevant internal representations are learned automatically from raw data.

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Non-negative Matrix Factorization

A dimensionality reduction technique that produces part-based and interpretable representations in non-negative data.

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Autoencoder-Based Dimensionality Reduction

An approach that learns lower-dimensional representations of data through neural-network-based compression.