graph neural networks

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...

A framework for brain tumor (Glioma) analysis using SVM and graph convolutional 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...

Title: Graph-Based Machine Learning Knowledge Distillation

Knowledge distillation is a technique for transferring knowledge from a large, complex model to a smaller, more efficient model. This paper presents a novel graph-based knowledge distillation method that leverages the inherent structure of neural networks to effectively transfer knowledge. We demonstrate that the use o...

Contextual Graph Neural Networks with Episodic Memory

This paper introduces Contextual Graph Neural Networks (CGNNs) with Episodic Memory, a novel approach to graph representation learning that addresses the limitations of standard Graph Neural Networks (GNNs) in handling contextual information across multiple graph instances. The core claim is that conventional GNNs stru...

A semi-supervised graph learning approach based on dual-stream gated fusion for industry equipment fault diagnosis under extreme label scarcity

This paper proposes the dual-stream gated fusion network coupled with a label propagation system (DSGF-LPS), a semi-supervised graph learning framework integrating robust graph construction, label propagation, and dual-stream gated fusion.

Neuro-Symbolic Reasoning with Attention-Based Knowledge Graphs

This paper proposes a novel approach to neuro-symbolic reasoning that addresses the challenges of seamless integration between neural networks and symbolic knowledge graphs. The core idea is to introduce an attention mechanism within the neural network architecture, allowing it to dynamically select and prioritize rele...

Dynamic Adaptive Graph Neural Networks: A Novel Approach to Graph Representation and Reasoning

Dynamic Adaptive Graph Neural Networks (D-AGNNs) represent a significant advancement in graph neural network design, focusing on the ability to dynamically adjust the graph structure to optimize both representation and reasoning capabilities. This paper introduces a novel algorithmic framework, leveraging adaptive algo...