graph neural networks

##基于图卷积神经网络的社交网络舆情分析

This paper investigates the application of Graph Convolutional Neural Networks (GCNs) for social network sentiment analysis. Traditional methods for analyzing public opinion often struggle with the complex, interconnected nature of social networks and the dynamic patterns of information dissemination. This research pro...

Multi-Granularity Graph Neural Network for Satellite-Assisted Marine Environmental Field Reconstruction over Sparse Observation Grids

Marine remote sensing combines broad-coverage satellite observations of the ocean surface with sparse in-situ and underwater observations. However, reconstructing continuous three-dimensional environmental fields remains challenging because of irregular sampling, vertical non-stationarity, and bathymetric barriers. In...

基于自适应的图论推理的特征选择

This paper introduces a novel approach to feature selection based on adaptive graph theory reasoning. Traditional feature selection methods often rely on static models, limiting their effectiveness in complex graph-based problems. We propose a framework leveraging the adaptive nature of graph structures and parameters...

The Wisdom Continuum

The Wisdom Continuum: A Unified Theory of Human Development, Learning, Intelligence, Artificial Intelligence, and Civilizational Evolution Description / Abstract The Wisdom Continuum presents a civilizational-scale framework that unifies human development, moral philosophy, educational reform, and artificial intelligen...

##基于知识图谱的程序漏洞预测

This paper proposes a novel approach to program vulnerability prediction by leveraging knowledge graphs. The core idea is to construct a program vulnerability knowledge graph, integrating code semantics and vulnerability data. We utilize Graph Neural Networks (GNNs) to analyze the relationships between code and vulnera...

Artificial-intelligence-driven garment simulation: Advances, challenges, and future perspectives

This framework facilitates a fully automated finite-element simulation workflow, significantly reducing the modeling threshold while improving efficiency and accuracy, thus offering a new, efficient, and cost-effective paradigm for the digital transformation of the apparel industry.

Adaptive Constraint Representation in Graph Neural Networks

This paper introduces an innovative adaptive constraint representation system for graph neural networks (GNNs). Traditional GNNs rely on fixed constraint representations, which often limit their ability to effectively model complex relationships within graphs. We propose a novel mechanism that dynamically adjusts the r...

Title: Dynamic Topology Generation via Graph Neural Networks

Dynamic topology generation is a fundamental challenge in network design, crucial for applications ranging from computer networks to biological systems. This paper investigates the application of graph neural networks (GNNs) to automate the creation of complex network topologies, offering a paradigm shift from manual d...

Challenges in Large‐Scale Graph Neural Networks in Topological Indices on Family of Graphs

基于图的图神经网络的动态参数调整

This paper introduces a novel dynamic parameter adjustment method for Graph Neural Networks (GNNs). Traditional GNNs often rely on static hyperparameters, limiting their adaptability to evolving data. We propose a framework where the GNN's graph structure is dynamically adjusted through a self-adaptive algorithm, allow...

Graph-Based Machine Learning Algorithm

This paper presents a novel graph-based machine learning algorithm designed to automate feature learning and enhance the performance of graph neural networks. Traditional graph neural network approaches often rely on handcrafted features, limiting their adaptability to complex graph structures. Our algorithm leverages...

基于图神经网络的复杂系统建模 - 动态适应性网络

This paper introduces a novel method for complex system modeling leveraging Graph Neural Networks (GNNs). Traditional approaches often require extensive redesign, while this method dynamically adjusts network structure and node weights, enabling a more adaptable representation of complex systems. We propose a GNN archi...