Tag
City2Graph is a new Python library that converts geospatial data into heterogeneous graphs for spatial analysis and Graph Neural Networks, with a peer-reviewed paper published in Computers, Environment and Urban Systems. It supports morphological, transport, mobility, and proximity graph construction, integrating with PyTorch Geometric and other graph tools.
This paper introduces MiGHT-EHR, a multi-task graph transformer for heterogeneous temporal EHR data, jointly modeling clinical entities, temporal trajectories, and task dependencies. It outperforms state-of-the-art methods on MIMIC-III and MIMIC-IV across drug recommendation, length-of-stay, mortality, and readmission prediction.
PolyUQuest is a verifiable, structure-aware web RAG framework that uses heterogeneous graphs to unify hyperlink topology, DOM hierarchy, and entity-relation knowledge, outperforming existing systems on answer correctness, coverage, and faithfulness.
This paper introduces RelAD, a reconstruction-based framework for detecting anomalies in relational databases by jointly modeling attribute and relational edge reconstruction. Extensive experiments on six new benchmarks show RelAD outperforms existing methods.
This paper presents a scalable heterogeneous graph neural network workflow for data-driven optimal power flow surrogate modeling, using distributed training on supercomputers and demonstrating improvements via fine-tuning pretrained models.