graph neural networks

Graph Neural Networks for Optimization

HistoNav: AI-Based H&E Histopathology Predicts Disease-Specific Survival and Guides Adjuvant Chemotherapy Decisions in Stage II/IIIA Colorectal Cancer

Background: Stage II/IIIA colorectal cancer (CRC) patients have a relatively high 5-year overall survival rate after surgical resection alone, but more than 50% of patients receive adjuvant chemotherapy. This study investigates a technology that applies artificial intelligence (AI) to conventional histopathology images...

Dynamic Graph-Based Machine Learning Algorithm

This paper introduces a novel machine learning algorithm, termed the Dynamic Graph-Based Machine Learning Algorithm, designed to enhance the performance of graph neural networks (GNNs) through adaptive graph structure learning. Traditional GNN approaches often rely on pre-defined graph structures, limiting their applic...

Decentralized Federated Learning with Byzantine Fault Tolerance using Graph Neural Networks

Federated learning (FL) offers a promising approach to training machine learning models on decentralized data without directly exchanging the data itself. However, FL systems are vulnerable to Byzantine attacks, where malicious participants introduce corrupted data or models to compromise the learning process. This pap...

#基于多模态嵌入的上下文感知推理

This paper explores a novel approach to contextual reasoning by leveraging multi-modal embeddings. The core idea is to learn deep embeddings from diverse data modalities – including text, images, and audio – and subsequently establish contextual relationships between them. We propose a framework that utilizes multi-mod...

Topological Vibro-Polaritonic Reaction-Manifold Compiler (TVPRMC): A First-Principles Architecture for Programmable Reaction-Space Control and Near-Universal Nanofabrication

This research monograph develops the Topological Vibro-Polaritonic Reaction-Manifold Compiler (TVPRMC), a speculative but conservation-law-respecting theoretical architecture for massively parallel fabrication with molecular and atomic-scale precision. The central hypothesis is that a fabricator need not position every...

##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...

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 &...