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 ...

The demand for immersive, realistic graphics in mobile gaming and AR or VR is pushing the limits of mobile hardware. Achieving lifelike simulations of fluids, cloth, and other materials historically ...

In the AI era, pure data-driven meteorological and climate models are gradually catching up with and even surpassing traditional numerical models. However, significant challenges persist in current ...

A World Bank study introduces an AI-based method using graph neural networks to break down national statistics like GDP into ...

Graph neural networks (GNNs) have gained traction and have been applied to various graph-based data analysis tasks due to their high performance. However, a major concern is their robustness, ...

Graph theory and computational modeling reveal that neural network architecture biases the male Caenorhabditis elegans brain toward prioritized sexual behaviors.

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

Researchers have introduced ChemGraph, an AI-powered agentic framework that automates and streamlines computational chemistry and materials science workflows. Combining graph neural networks for ...

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 ...