Graph neural networks (GNNs) have rapidly emerged as a central methodology for analysing complex datasets presented as graphs, where entities are interconnected through diverse relationships. By ...

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 ...

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 ...

Researchers have proposed a Fourier graph neural network for estimating the state of health of lithium-ion batteries while simultaneously considering spatial and temporal feature relationships. The ...

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

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 ...

From Facebook friend circles to hidden influencer groups, community detection in social networks is evolving fast. Researchers are combining deep learning, graph neural networks, and advanced ...

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 ...