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 ...

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 ...

BingoCGN employs cross-partition message quantization to summarize inter-partition message flow, which eliminates the need for irregular off-chip memory access and utilizes a fine-grained structured ...

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As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...

A new research model called PiGRAND merges physics guidance with graph neural diffusion to predict and control AM processes.

Researchers at Chiba University in Japan have developed a new artificial intelligence framework capable of decoding complex brain activity with significantly improved accuracy, marking an important ...

Accurate stock trend forecasting is a central challenge in financial economics due to the highly nonlinear and interdependent nature of market dynamics. Traditional statistical and machine learning ...

Urban congestion is a big problem in our cities. It leads to commuter delays and economic inefficiency. More tragically, ...

A team of young engineers and a university student in Thessaloniki has developed an award-winning AI-driven system to ...