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 ...

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 ...

AIbase基地于2026年4月14日发布的一篇AI新闻报道称，研究人员推出了一种名为HarmonyGNN的全新训练技术，可显著提高图神经网络（Graph Neural Networks, GNNs）的准确性。GNN是一种用于处理图数据的人工智能系统，广泛应用于药物发现和天气预测等领域。图数据由节点（数据点）和边（连接线）组成，其中边表示节点之间的关系。这些关系可以是相似的（同构性）或不同的（异构 ...

The increasing complexity of modern chemical engineering processes presents significant challenges for timely and accurate anomaly detection. Traditional ...

Researchers have demonstrated a new training technique that significantly improves the accuracy of graph neural networks (GNNs)—AI systems used in applications from drug discovery to weather ...

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 ...

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 ...

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 ...