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 ...

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

近日，西安交通大学杨瑞娜教授团队的研究成果《Eliminating Social Popularity Bias in Recommendation: Causal Inference-Based Social Graph Neural Networks》正式发表于管理学顶级期刊《INFORMS Journal on ...

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 ...

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

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 ...

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 ...

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 ...

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

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