graph neural networks

Insider Threat Prediction Using Graph Analysis

The findings demonstrate the effectiveness of the graph-based methods in providing high-quality behavioral dependencies, with better accuracy and fewer false alarms and greater explainability in real-life insider risk monitoring.

Contextualized Semantic Graph Neural Networks

This paper introduces Contextualized Semantic Graph Neural Networks (CSGNNs), a novel approach to graph representation learning that addresses the inherent limitations of static graph embeddings. Existing graph neural networks often struggle to adapt to evolving semantic relationships within graphs, primarily due to th...

基于自适应神经网络的图论推理

This paper introduces a novel graph theory reasoning framework based on adaptive neural networks. Traditional graph theory reasoning methods often rely on fixed models, limiting their effectiveness in complex graph problems. Our proposed approach leverages adaptive neural networks to address these limitations, offering...

基于图神经网络的拓扑分析

This paper introduces a novel approach to topology analysis utilizing graph neural networks (GNNs). We propose a method for automatically constructing and analyzing complex topology graphs, with the capability to predict and optimize topological structures. Traditional approaches rely on manual graph construction, whic...

LibXSub: A Framework for Detecting Binary Third-Party Library Reuse Regions Based on Explanatory Subgraphs

LibXSub introduces an explainable subgraph generation mechanism that identifies critical call relationships through edge masks and characterizes the contribution of different function feature dimensions to region similarity prediction through node feature masks, demonstrating the effectiveness of the proposed framework...

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...