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A visual guide to how graph neural networks work under the hoodContinue reading on Level Up Coding »
A visual guide to how graph neural networks work under the hood The post Graph Neural Networks: GCN, MPNN, and GAT, Explained Simply appeared first on Towards Data Science.
The telecommunications industry is currently undergoing a paradigm shift, moving from traditional manual human-driven operations to fully Autonomous Network Operations. Modern netw...
A new spatio-temporal graph network models players and the ball as a dynamic graph, detecting key match events and tactical patterns with record accuracy and real-time speed.
How calibrated decision models can handle high-frequency graph decisions while LLMs remain focused on reasoning, synthesis, and open-ended generation. The post GraphRAG with TypeSa...
Beyond basic graph retrieval: six production-oriented architectures for combining semantic search, knowledge graphs, and LLM reasoning. The post GraphRAG: A Practitioner's Guide to...
A new conditioned diffusion-based graph attention network from the University of Naples Parthenope delivers probabilistic spatio-temporal forecasts with calibrated uncertainty, ran...
by Ritwik Ganguly, Sana Aafrine, Sk Md Mosaddek Hossain, Sumanta Ray A central challenge in downstream single-cell RNA sequencing (scRNA-seq) analysis is the high-dimensional, sma...
I built four AI retrieval architectures on a laptop and benchmarked them against the same set of documents and questions. Here’s what the results taught me about the trade-offs bet...
In this article, you will learn how to benchmark a deterministic 3-Tiered Graph-RAG system against a standard vector RAG pipeline on fact-dense queries, and what...
Let the AI decide which agents to run — and run the right ones simultaneouslyContinue reading on Data Science Collective »
by Anchi Sun, Yongjing Hao, Yijie Ding, Jing Chen, Hongjie Wu G protein-coupled receptors (GPCRs) are central membrane receptors and major therapeutic targets. However, predicting...
AI VK Research разработала метод, который позволяет масштабировать графовые нейросети на сотни миллионов объектов и миллиарды связей.
The Fly Language Model (FLM) drives all 166,700 retained neurons and 25.6 million edges of the MaleCNS fruit fly connectome with token embeddings, then adds a small learned correct...
Group interactions are everywhere in the real world. Researchers co-author papers, shoppers buy products together, students hang out in clusters between classes, and users on quest...
Metabolomics, the large-scale study of small molecules in biological systems, has long faced a stubborn interpretability problem. Researchers can measure thousands of metabolites a...
A viral debate over loops versus graphs points to a bigger shift in how we build AI systems. Here’s what graph engineering actually means, how it differs from prompt, context, and...
Researchers have introduced HGA-Net, a hypergraph-based adapter that connects related examples within each mini-batch and achieves near-perfect fine-grained recognition with only 1...
Many of the questions that matter in enterprise data aren't just about individual rows — they're about how things connect: how two accounts are linked, what path a payment took, wh...
NVIDIA Kumo, now open source, runs on Snowflake ML so teams can train graph neural networks on relational data with GPUs, all inside Snowflake.
Revisiting gradient-directed Monte Carlo — from molecular design to a 16-value weight grid.Continue reading on Medium »
New Graphwise Platform capabilities to be showcased live at the Graphwise AI Summit 2026
Learn how to leverage NVIDIA’s cuDNN Frontend Graph API to build custom kernel fusions, autotuning engine configurations, FP8-style epilogues, scaled dot-product attention, dynamic...
Z.ai and Qwen independently shipped near-identical architectures: 3:1 linear hybrids, compressed indexers, gated residuals, and Muon training. The post GLM-5.3-Flash vs Qwen3.8-Fla...
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