Fine-grained spatial data are critical for informed decision-making in domains ranging from economic planning to ...

Researchers have demonstrated a new training technique that significantly improves the accuracy of graph neural networks ...

Graph Neural Networks (GNNs) and GraphRAG don’t “reason”—they navigate complex, open-world financial graphs with traceable, multi-hop evidence. Here’s why BFSI leaders should embrace graph-native AI ...

Neo4j has expanded its Google Cloud integration with new features aimed at making graph-powered AI agents and analytics more accessible. Enhancements include native Neo4j AI Agent access in Gemini ...

Researchers have proposed a Fourier graph neural network for estimating the state of health of lithium-ion batteries while ...

The multiple condition (MC)-retention model is an uncertainty-aware graph-based neural network that predicts liquid chromatography (LC) retention times across multiple column chem ...

A universal potential for all-purpose atomic simulations has been pursued for decades, but remains challenging due to limitations in model expressiveness and dataset construction. Now, writing in the ...

By observing how health data changes over time, artificial intelligence can help identify “tipping points” when a patient’s body is moving toward disease. That is according to an editorial published ...