arXiv:2606. 24978v1 Announce Type: new Abstract: Accurate particulate matter (PM) prediction is crucial for mitigating air pollution.
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.
This study presents a new mobile‑sensing dataset from Surat, India, capturing PM2.5 concentrations along with meteorological and land‑use variables. The authors model the data as a graph using two node‑definition strategies—uniform segmentation and DBSCAN clustering—and introduce a Spatially Attentive Graph Neural Network (SA‑GNN) that combines cluster‑specific GRUs with a Graph Attention Network to forecast fine‑grained, short‑term PM2.5 levels. SA‑GNN outperforms traditional LSTM, RNN, GRU, and ANN baselines, achieving an R² of 0.95, RMSE of 6.8, and MAE of 4.2 µg/m³ on the dataset.
arXiv:2607. 11896v1 Announce Type: cross Abstract: Forecasting particulate matter (PM10) requires both station-scale accuracy and continuous spatial fields, especially during severe dust storms.
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.
arXiv:2605.03795v3 Announce Type: replace-cross Abstract: Urban air quality forecasting is challenging because pollutant concentrations are nonlinear, nonstationary, spatiotemporally dependent, and o...
arXiv:2412. 19419v2 Announce Type: replace-cross Abstract: Graph neural networks are deep neural networks designed for graphs with attributes attached to nodes or edges.
The paper presents a comparative analysis of six state‑of‑the‑art counterfactual explainers for graph neural networks, focusing on methods that can both add and remove edges to alter model predictions. It evaluates these explainers across diverse real‑world and synthetic datasets, covering binary and multi‑class graph and node classification tasks, using a range of quantitative and qualitative metrics. The study highlights the trade‑offs between explanation size, coverage, and quality, aiming to pinpoint each method’s strengths and weaknesses to inform future research.
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.
arXiv:1905. 11395v2 Announce Type: replace Abstract: Graph convolution network based approaches have been recently used to model region-wise relationships in region-level prediction problems in urban computing.
arXiv:2606. 03495v1 Announce Type: new Abstract: Heterogeneous graph neural networks (HGNNs) have demonstrated remarkable performance in modeling complex relational data, however their interpretability in high-stakes applications remains a critical challenge.
arXiv:2605.06814v2 Announce Type: replace Abstract: Graph neural networks (GNNs) increasingly rely on sophisticated architectures and training procedures to achieve desirable properties such as high...
arXiv:2607. 26404v1 Announce Type: new Abstract: Graph Neural Networks (GNN) facilitate effective prediction on graph data such as molecules, media networks and neural network blueprints.