graph neural networks

BioSurveillance-Agent: A Multi-Method Explainable Agentic Platform for Epidemic Surveillance on One-Health Networks

How much does the graph help? An information-parity evaluation of graph neural networks for urban air-quality estimation

Graph neural networks (GNNs) are increasingly used to estimate air pollution at unmonitored urban locations, but reported gains often conflate what a model is with what it sees. We present a heterogeneous, wind-aware graph attention network that embeds regulatory monitoring stations and traffic counters as distinct nod...

Blockchain fog attention framework for collusion detection and automated accountability in internet of things networks

The proposed Blockchain-Fog Computing Collaborative Framework with Deep Attention-based Collusion Detection and Automated Accountability (BF3-ACDA) framework combines a hierarchical blockchain–fog architecture with an attention-based collusion graph neural network (AttnCol-GNN), whose reputation-aware attention coeffic...

Multimodal Graph Neural Networks and Evolutionary Knowledge Fusion for Secure and Explainable Governance in Intelligent IoT Interactive Media Systems.

This research proposes a secure, explainable, and context-aware governance framework for blockchain-based digital media contracts in multimodal artificial intelligence-enabled AIoT interactive systems. As digital licensing, NFT copyright management, royalty distribution, and cross-chain content circulation become incre...

Solar Photovoltaic Generation Forecasting: A Review of Artificial Intelligence Approaches

The technical synthesis finds that classical machine learning remains competitive for day-ahead forecasting with well-structured numerical weather prediction inputs, that convolutional neural network–long short-term memory (CNN-LSTM) hybrids dominate the deep-learning literature, and that graph-based and transformer ar...

Learning Subgroup Relations Using Siamese Graph Neural Networks

Determining whether one finite group is isomorphic to a subgroup of another is a fundamental problem in computational group theory. In this work, we propose a Siamese Graph Neural Network (Siamese GNN) for subgroup prediction using Cayley graph representations of finite groups. Each input group is represented by its un...

A Parallel Convolutional–Hypergraph Feature learning Network for pulmonary nodule classification

Revisiting topological bias in imbalanced graph learning: A Dual-Balanced Graph Neural Network approach

Learning Calibrated and Transferable Node-Level Infection Risk in Stochastic Epidemics on Complex Networks

基于动态拓扑记忆网络的神经符号推理

This paper proposes a novel approach to neural-symbolic reasoning by introducing a Dynamic Topological Memory Network (DTMN). The core idea is to construct a memory network capable of dynamically adjusting its internal topology to mirror the complexity and relationships within the input data. This addresses a key limit...

Intelligent Transportation Systems: A Review of Integration of Digital Twin and Machine Learning Control

This paper presents a comprehensive review of the integration of Intelligent Transportation Systems (ITS) and Digital Twin (DT) technologies for intelligent traffic management, and identifies key research gaps and outlines future research directions toward scalable, reliable, adaptive, and real-time DT-enabled ITS arch...

A Lightweight Explainable AI Framework for Early Brain Tumor Detection Using EEG Functional Connectivity

The findings support the feasibility of lightweight graph-based EEG analysis as an auxiliary screening and decision-support approach and at the same time, MRI and CT remain necessary for clinical confirmation and anatomical characterization.