June 01, 2026

Introduction to Graph Neural Networks (Synthesis Lectures on Artificial Intelligence and Machine Learning)

23 minutes

A comprehensive introduction to Graph Neural Networks (GNNs), a specialized class of deep learning models designed for non-Euclidean data structures. While traditional models like CNNs and RNNs excel at processing grids and sequences, GNNs are uniquely capable of capturing the complex relational information found in social networks, molecular structures, and traffic systems. By combining graph topology with node feature propagation and aggregation, GNNs generate high-quality representations of data points. The documentation details the mathematical foundations required for these models, including linear algebra, probability theory, and graph theory. It further explores the evolution from vanilla GNNs to advanced variants such as convolutional, recurrent, attention, and residual networks. Finally, the sources outline real-world applications across various fields—ranging from chemistry and physics to knowledge graphs and recommender systems—while identifying current research limitations and future directions.

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