arXiv AI

Spatial Prediction of Soil Microplastics and Organic Matter Using Graph Attention Networks

arXiv:2607. 22875v1 Announce Type: cross Abstract: Accurate estimation of soil microplastics and organic matter is essential to assess ecosystem health and support sustainable land use.

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 16

Multi-Modal Spatio-Temporal Graph Neural Network with Mixture of Experts for Soil Organic Carbon Prediction

arXiv:2606. 16580v1 Announce Type: new Abstract: Top-soil organic carbon (SOC) prediction is fundamental to agricultural sustainability, land use policy and fertilization planning.

By Daniele Mos, Felipe Drummond, Anton Bossenbroek, Soufiane el Khinifri

rag fine-tuning multimodal

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 Machine Learning

Jun 19

Enhancing Graph Neural Networks Using Proximity Graphs for Dust Source Emission Forecasting

arXiv:2606. 19825v1 Announce Type: new Abstract: Accurate prediction of dust source emissions is critical for mitigating the significant environmental and health hazards posed by dust storms.

By Maryam Sanisales, Zahed Rahmati, Ali Darvishi Boloorani, Ali Vefghi

arXiv Machine Learning

Jun 2

AdaKernel: Learning Adaptive Kernel Parameters for Spatiotemporal Graph Neural Networks

arXiv:2606. 01283v1 Announce Type: new Abstract: Modeling spatial dependencies is central to spatiotemporal data analysis using Graph Neural Networks (GNNs).

By Zhongyue Zhang, Guangyin Jin, Yuxuan Liang, Suwan Yin, Yuankai Wu

arXiv Machine Learning

Jun 18

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.

By Minwei Zhao, Sanja Scepanovic, Stephen Law, Ivica Obadic, Cai Wu, Daniele Quercia

rag safety

arXiv Machine Learning

Jun 19

SEAGAN: domain-Specific and Edge-Aware Graph Attention Network for Dynamic Plant Processes

arXiv:2606. 19623v1 Announce Type: new Abstract: Graph neural networks (GNNs) provide a flexible framework for learning from scientific data linked through physical, biological, or functional relationships.

By Antriksh Srivastava, Soumyashree Kar

benchmarks

arXiv Machine Learning

1d ago

EquiPocket: an E(3)-Equivariant Geometric Graph Neural Network for Ligand Binding Site Prediction

arXiv:2302. 12177v4 Announce Type: replace-cross Abstract: Predicting the binding sites of target proteins plays a fundamental role in drug discovery.

By Yang Zhang, Zhewei Wei, Ye Yuan, Chongxuan Li, Wenbing Huang

benchmarks

arXiv AI

Jun 9

EssentialGIN: a new approach for gene essentiality prediction based on graph isomorphism neural networks

arXiv:2606. 07700v1 Announce Type: cross Abstract: Background: Prediction of essential genes (proteins), is a basic and challenging problem but at the same time very costly and time-consuming in wet-lab experiments.

By Sahar Mansouri-Rad, Zahra Narimani, Parvin Razzaghi, Nazanin Hosseinkhan

rag

arXiv Machine Learning

Jun 29

Effects of relational graph modularity and depth on the learning performance of neural networks

arXiv:2507. 10005v2 Announce Type: replace Abstract: In recent years, graph-based machine learning techniques, such as reinforcement learning and graph neural networks, have garnered significant attention.

By Yash Arya, Sang Hoon Lee

computer-vision reinforcement-learning

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 AI

Aug 10

Cluster Attention for Graph Machine Learning

arXiv:2604. 07492v2 Announce Type: replace-cross Abstract: Message Passing Neural Networks have recently become the most popular approach to graph machine learning tasks; however, their receptive field is limited by the number of message passing layers.

By Oleg Platonov, Liudmila Prokhorenkova

llms benchmarks

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