2026-10-18: Introduction to Graph Neural Networks 🌐

An interactive article on Distill explains modern graph neural networks (GNNs), building a GNN step by step from a basic implementation to a state-of-the-art model.

Graphs represent real-world objects through their connections, and GNNs operate on graph data with applications in antibacterial discovery, physics simulations, fake news detection, traffic prediction, and recommendation systems.

The article covers graph types, problem domains, challenges in representing connectivity, components of GNNs, and includes a playground for molecular odor prediction.

It also delves into advanced topics like multigraphs, sampling, attention, and generative modeling. The article is free to read online.

🌎 Источник

✍️ by data_science_weekly

#news #ai #data
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