graph neural networks

Deterministic Topological Retrieval-Augmented Generation via Discrete Dynamical Basins

Retrieval-Augmented Generation (RAG) systems navigate a fundamental trade-off between the computational simplicity of flat vector indices and the structural richness of graph representations. Standard dense retrieval collapses document discourse into isolated embeddings, causing semantic fragmentation and context loss....

Integrating Low-Cost xTB Electronic Priors for Predicting Electronic and Excited-State Properties of Organic Conjugated Molecules.

Accurate prediction of electronic and excited-state properties is important for the computational screening of organic conjugated molecules. However, this task remains challenging because these properties depend on both local chemical environments and global structural and electronic information. In this work, we devel...

Coverage and Protocol-Aware Evaluation of Risk Enrichment and Graph-Tabular Learning for Anti-Money Laundering and Fraud Detection

Reported gains from graph-based machine learning in anti-money laundering and fraud detection can vary substantially with the historical evidence available to a model and with the evaluation protocol used. This study introduces a coverage-aware and protocol-aware evaluation framework for assessing account-risk enrichme...

Explainable Relation-Heterogeneous Temporal Graph Neural Networks for Supply Chain Disruption Onset Prediction

Supply-chain disruption prediction is structurally different from ordinary time-series forecasting because risk is transmitted through typed dependencies such as sourcing, transportation, distribution, and alternate-supply relations. We present RHET-GNN, a relation-heterogeneous temporal graph neural network that coupl...

Reaction-Center-Guided Reranking of Transformer Product Predictions with a Product-Conditioned Graph Neural Network

PRECISION_paper: reproducibility package for interpretable drug repurposing in triple-negative breast cancer

First public release of the reproducibility package for the manuscript Heterogeneous graph neural networks with biological prior knowledge for interpretable drug repurposing in triple-negative breast cancer. It contains the full pipeline, the 34 numbered analysis scripts, the tables behind every number in the manuscrip...

Graph neural network-based predictive maintenance algorithm for safe operation of dust removal systems in coal-fired power plants

Ensuring the safety and reliability of coal-fired power plants under dynamic operating conditions remains a major industrial challenge. This paper presents a novel Graph Neural Network–based Predictive Maintenance (GNN-PdM) algorithm designed to model the spatiotemporal dependencies within dust removal systems for proa...

Improving mpox identification under imbalanced data using geometric transformation and color augmentation with graph neural networks

Mpox lesion identification from skin images remains challenging because lesion appearances often overlap with other dermatological diseases and available data are commonly imbalanced. This study proposes a graph-based multi-class classification framework that combines class-balancing augmentation, convolutional neural...

Autonomous Ground Vehicles in Robotics: Kinematics, Dynamics, and Fleet Control

An open-access curriculum module examining the kinematics, dynamic control, perception architectures, and multi-agent coordination of Autonomous Ground Vehicles (AGVs). Details unicycle (differential-drive) and Ackermann bicycle steering models, tire-road lateral friction boundaries, Stanley and Pure Pursuit path-track...

Graph neural network-based risk stratification of prostate cancer using gene expression and SHAP interpretability

Accurate risk stratification is essential for guiding treatment decisions and preventing over treatment of prostate cancer, which remains one of the most prevalent cancers among adult men. While the Gleason score, obtained from prostate biopsies, is routinely used to assess tumor aggressiveness, the biopsy procedure ca...

Application of Artificial Intelligence in Satellite Orbit and Clock Correction: A Review

Aims: This review examines AI-assisted methods for satellite orbit determination and prediction, clock-bias forecasting, thermospheric-density calibration, atmospheric-delay modelling, and integrated GNSS correction. It identifies recurrently successful approaches, limitations in the current evidence, and priorities fo...

Artificial intelligence-enabled cross-scale integration of traditional Chinese medicine and biomedicine for sepsis: from mechanisms to delivery

A conceptual framework for an integrated “target recognition–drug matching–carrier design–subtype adaptation” decision model is proposed, which may inform the matching of combined biomedical and botanical drug regimens with nanodelivery systems based on patient molecular subtypes.