graph neural networks

##基于图神经网络的复杂性建模

This paper introduces a novel framework for complex system modeling based on graph neural networks (GNNs). We propose a method to automatically learn complex system structures from data, facilitating efficient complexity analysis. Traditional approaches often require manual model design, while GNNs offer a powerful mec...

Beyond Model Complexity: A Reproducible Comparison of Classical Machine Learning, Matrix Factorization, Graph Embeddings, and LightGCN for Recommendation

Findings show that under the evaluated setting, greater model complexity did not consistently translate into higher recommendation effectiveness, and they thus highlight the importance of strong baselines, model tuning, standardized evaluation, and reproducible experimental protocols.

A privacy-preserving federated spatiotemporal dynamic graph neural network framework for epileptic seizure prediction

Abstract Epilepsy ranks among the most prevalent and debilitating neurological disorders, globally affecting an estimated 50 million individuals across all age groups and socioeconomic backgrounds [WHO, 2019]. In recent years, research on its prediction methods has made significant progress, driven by advancements in a...

Yield-graph: multi-stage growth-aware maize yield prediction via graph neural networks

Activated permutation entropy for graph signals

Activated Permutation Entropy is introduced and the development of improved graph-based entropy methods that enable nonlinear analysis of broader data types and establishing relationships with emerging ideas in data science is expected to accelerate.

A Fourier physics-informed graph neural network for Allen–Cahn and Conservative Allen–Cahn equations

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...