graph neural networks

Multi-Modal Knowledge Graph Reasoning with Causal Relation Modeling

This paper addresses the limitations of traditional knowledge graph reasoning, which primarily relies on correlational relationships, leading to potentially biased inferences. We propose a novel approach to multi-modal knowledge graph reasoning by explicitly modeling causal relationships between entities. Our core clai...

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...

Stratified Sampling and Graph Neural Networks for Zero‐Day Attack Detection

High-Dimensional Data Structures in Adaptive Neural Networks

This paper introduces a novel adaptive graph neural network (GNN) architecture designed specifically for high-dimensional data. Traditional GNNs often struggle with the complexity of high-dimensional data, leading to slow convergence and suboptimal performance. This work proposes a dynamically adjustable graph structur...

Brain-Universe Resonance in Grafov's Quantum Theory of Gravity (GQTG): Complete Theoretical Framework, Topological Mechanisms, Experimental Predictions, and Consistency with Empirical EEG Data. (Faraday-Cage Prediction).

Article 21 Brain-Universe Resonance in Grafov'sQuantum Theory of Gravity (GQTG): Complete Theoretical Framework, Topological Mechanisms, Experimental Predictions, and Consistency with Empirical EEG Data. (Faraday-Cage Prediction). (Extension and Full Development of Article 15) Author Yuri GrafovIndependent Physicist &...

##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...