graph neural networks

BEPGN: A boundary element-enhanced physics-informed graph neural network for solving Laplace boundary value problems

Source code for "Pseudo-label supervised graph neural network unveils robust, fine-grained, interpretable spatial domains"

Asynchronous intermittent control strategy for stability of stochastic delayed complex networks with deception attacks based on Dupire’s functional Itô formula

This paper investigates the mean-square exponential stability of stochastic delayed complex networks under asynchronous intermittent control with deception attacks (AICDA). Because different nodes have different dynamics, each node requires its own independent intermittent control. Meanwhile, sensor–controller and cont...

Cycle-Aware Algorithms for Community Detection and Statistical Network Inference: A Review and Tutorial

Community detection is often formulated through edge density, yet many scientifically meaningful groups are characterized by closure, redundancy, and repeated multi-step interaction. This structured review develops an algorithmic and statistical perspective on cycle-aware network analysis, connecting motif and cycle co...

Open Science for Molecular Modelling with the Open Force Field Initiative

Drawing on computational methods that are based around training to extensive condensed phase physical property and quantum mechanical datasets, I will describe some of our efforts to design accurate and transferable inter- and intra-molecular potentials, with a view to applications in condensed phase atomistic modellin...

Integrating Danger Theory into Graph Neural Networks for Early Warning Fake News Detection

The rapid propagation of fake news on social media poses a significant threat to society, demanding detection methods that are not only accurate but also timely. While Graph Neural Networks (GNNs) are powerful tools for modeling propagation cascades, they often struggle in early detection scenarios where structural inf...

Dual-Modality Neural Network Integrating Sequence and Structural Information for Drug-Target Interaction Prediction

Transforming Healthcare with Motion Capture and AI

This chapter presents a comprehensive analysis of the fundamental shift in healthcare driven by the convergence of high-fidelity motion capture (MC) and artificial intelligence (AI). This review is intended for clinicians who recognize the potential benefits of digital motion analysis but seek evidence supporting its a...

Cross-agency financial fraud detection based on federated graph neural networks and privacy protection

This method constructs individual financial graphs using accounts, invoices, devices, terminals, and transaction relationships, and employs federated secure aggregation for joint modeling across organizations to address the discretization, isolation, and privacy protection issues in cross-organizational financial fraud...

Application of Reinforcement-Based Learning Feature Selection for Early Detection of Alzheimer's Disease Using Speech

The early detection of Alzheimer&s;s disease (AD) enables timely intervention methods. Speech-based biomarkers, when combined with advanced machine learning (ML) models and feature selection, provide scalable and promising diagnostic results. In our study, we present a novel speech-based feature selection framework usi...

Extracting interpretable single-cell metabolic states with graph-guided representation learning

Metabolism shapes cellular function and state, yet measuring single-cell metabolic states at scale remains a challenge. We present Metabolic Representation Net (MeRN), a graph-guided variational autoencoder that leverages prior metabolic knowledge as a topology graph to learn latent representations of metabolic state a...

Physics-Informed Surrogate Model Using Graph Neural Network for Cardiac Electrophysiology