graph neural networks
3 stories filed under graph neural networks on Beyond Market Intelligence. The newest of them: “How AI agents doubled their footprint at NeurIPS in one year”, “Explore machine learning in a focused math community, free for all”, and “Explore How Graph Neural Networks Unlock Smarter Data Connections”. AI agents doubled their footprint at NeurIPS in one year, from 2.9% of papers in 2025 to 6.8% in 2026, or 170 to 620 papers. Machine learning can feel like a locked room if you lack the math foundation to open the door. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every graph neural networks story on Beyond Market Intelligence, newest first.
How AI agents doubled their footprint at NeurIPS in one year
AI agents doubled their footprint at NeurIPS in one year, from 2.9% of papers in 2025 to 6.8% in 2026, or 170 to 620 papers. That's a signal worth watching. Using Qwen3-Embedding-0.6B, Hadi Mohammadi embedded every abstract from both years, grouped them into 150 topics, and mapped the whole conference. The result is an interactive tool where you can search, color by change or city, and click through. Graph neural networks shrank. Benchmarks rose. The data tells a clear story.
Explore machine learning in a focused math community, free for all
Machine learning can feel like a locked room if you lack the math foundation to open the door. This workshop, hosted on the (blue) Math Discord server, changes that. Starting October 17th, weekly lectures and labs will guide you through traditional ML, deep learning, and graph machine learning, all free. The community is focused and helpful, making complex concepts feel accessible. If you know Python and basic calculus, you are ready to join.
Explore How Graph Neural Networks Unlock Smarter Data Connections
Graph neural networks can feel like a black box, but they don't have to. This visual guide breaks down GCN, MPNN, and GAT by showing exactly how they pass messages and weigh connections. It's a clear, practical look at the mechanics, and it's perfect for anyone ready to move beyond spreadsheet mental models. For a broader perspective on how AI structures complex information, you might also explore our related guide on paragraph structure in LLMs.