Abstract: Accurate prediction of socio-political events is a longstanding challenge with profound implications for risk management, policy planning, and international relations. Traditional machine ...

Abstract: The integration of Large Language Models (LLMs) with Graph Neural Networks (GNNs) has recently been explored to enhance the capabilities of Text Attribute Graphs (TAGs). Most existing ...

Official code for the CVPR 2026 paper: Bridging the 2D-3D Gap: A Hierarchical Semantic-Geometric Map for Vision Language Navigation Kailing Li, Tianwen Qian, Lijin Yang, Yuqian Fu, Jingyu Gong, ...

The resulting knowledge graph provides a robust framework for understanding sepsis, supporting clinical decision-making, and facilitating further research. The success of this approach underscores the ...

STM-Graph is a Python framework for analyzing spatial-temporal urban data and doing predictions using Graph Neural Networks. It provides a complete end-to-end pipeline from raw event data to trained ...