arXiv Machine Learning

UST-GNN: A Unified Spatial--Topological Graph Neural Network Framework for Urban Analytics--Demonstrated through a Case Study on Urban Health Prediction

arXiv:2504. 04739v3 Announce Type: replace Abstract: Understanding how social, demographic, environmental, and spatial factors jointly shape urban outcomes is essential for sustainable urban development and evidence-based policy.

arXiv Machine Learning

Jul 23

Predicting Groundwater Arsenic Concentrations Using Graph Neural Networks

arXiv:2607. 19392v1 Announce Type: new Abstract: Arsenic contamination in groundwater presents a longstanding public health crisis in the United States, especially for households depending on private wells.

By William Xing, Stephanie Yang, Aarush Bandemegal, Anushree Misra, Ananya Kalapatapu, Brennan Lagasse, Kevin Zhu

benchmarks

arXiv Machine Learning

Jun 5

Learning to model pediatric asthma exacerbation from multiple risk factors: a case study in coastal Virginia

arXiv:2606. 06174v1 Announce Type: new Abstract: Childhood asthma is a common illness exacerbated by air pollution as well as meteorological and neighborhood-level socioeconomic factors.

By Jonathan Colen, Eric Werner, Maryam Golbazi, Heather Richter, Diana McSpadden, Amy Quinn, Jocel Santos, Mary Jane Darling, Mary Margaret Gleason

safety

arXiv Machine Learning

Jun 5

CausalPOI: Spatio-Temporal Graph-Based Causal Modeling for Cold-Start POI Check-in Forecasting

arXiv:2606. 05413v1 Announce Type: new Abstract: As urban environments continue to evolve rapidly, accurately modeling the dynamic behaviour of Points of Interest is essential for supporting data-driven urban planning and commercial decision-making.

By Zhaoqi Zhang, Miao Xie, Yi Li, Linyou Cai, Siqiang Luo, Gao Cong

benchmarks

arXiv AI

Jun 9

OSMGraphCLIP: Learning Global Location Representations from OpenStreetMap Graphs

arXiv:2606. 08046v1 Announce Type: new Abstract: We present OSMGraphCLIP, a CLIP-style geospatial representation model that learns global location embeddings from freely available OpenStreetMap (OSM) data.

By Dimitrios Michail, Eleni Saka, Ioannis Giannopoulos, Ioannis Papoutsis

rag benchmarks safety

arXiv Machine Learning

Jun 5

HEIST: A Graph Foundation Model for Spatial Transcriptomics and Proteomics Data

arXiv:2506. 11152v4 Announce Type: replace-cross Abstract: Single-cell transcriptomics and proteomics have become a great source for data-driven insights into biology, enabling the use of advanced deep learning methods to understand cellular heterogeneity and gene expression at the single-cell level.

By Hiren Madhu, Jo\~ao Felipe Rocha, Tinglin Huang, Siddharth Viswanath, Smita Krishnaswamy, Rex Ying

llms rag benchmarks

arXiv Machine Learning

Jun 19

Towards Graph-Based Deep Learning for Map Generalization: Insights from Building Footprints Simplification and Aggregation

arXiv:2606. 19956v1 Announce Type: new Abstract: Map generalization remains one of the fundamental tasks in cartography, especially for the simplification and aggregation of complex building footprints.

By Yanning Wang, Zhiyong Zhou, Zhouyu Liu, Mengni Yu, Yu Feng

arXiv Machine Learning

Jun 25

Auto-Configured Explainable Graph Neural Networks for Multi-Site Pollution Prediction

arXiv:2606. 24978v1 Announce Type: new Abstract: Accurate particulate matter (PM) prediction is crucial for mitigating air pollution.

By Abdelkader Dairi, Fouzi Harrou, Ying Sun

arXiv AI

Jun 12

PlaceRep: Geospatial Place Representation Learning from Large-Scale Point-of-Interest Data

arXiv:2507. 02921v4 Announce Type: replace-cross Abstract: Learning effective representations of urban environments requires capturing spatial structure beyond fixed administrative boundaries.

By Mohammad Hashemi, Hossein Amiri, Andreas Zufle

rag benchmarks

arXiv Machine Learning

Jun 26

Graph Neural Networks Applications Across Domains: All Insights You Need

arXiv:2606. 27202v1 Announce Type: new Abstract: Graph neural networks have moved from a niche representation-learning technique to the default model class wherever data carry relational structure.

By Abderaouf Bahi

computer-vision safety

arXiv AI

Jun 9

SNR-ST-Mix: Sample-specific Neighborhood Regression Mixup for Augmented Spatial Transcriptomics Imputation with Deep Neural Network

arXiv:2606. 08712v1 Announce Type: cross Abstract: Purpose: Spatial transcriptomics (ST) enables gene expression measurements within the tissue context.

By Hongyi Yu, Yaoyu Fang, Jiahe Qian, Xinkun Wang, Lee A. Cooper, Bo Zhou

arXiv AI

Jun 30

Automated Quality Assessment of Geospatial Vector Data: A GeoAI Approach using Spatial Representation Learning

arXiv:2606. 28390v1 Announce Type: cross Abstract: Geospatial vector data quality is a foundational research topic in GIS, yet classic rule-based quality assessment algorithms often struggle with diverse urban morphologies and massive data volumes.

By Hao Li, Chen Chu, Filip Biljecki, Cyrus Shahabi, Wenwen Li

agents diffusion benchmarks data-engineering

arXiv AI

Jul 20

A Neuro-Symbolic Approach for Probabilistic Reasoning on Graph Data

arXiv:2507. 21873v2 Announce Type: replace Abstract: Graph neural networks (GNNs) excel at predictive tasks on graph-structured data but often lack the ability to incorporate symbolic domain knowledge and perform general reasoning.

By Raffaele Pojer, Andrea Passerini, Kim G. Larsen, Manfred Jaeger

benchmarks

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