graph neural networks

A privacy-preserving federated deep learning framework for real-time fraud detection in decentralized financial systems

The rapid growth of decentralized financial systems has introduced sophisticated fraud that conventional detection methods struggle to identify, due to fragmented data, strict privacy regulations, and the need for real-time response. This study proposes Federated Differentially Private Fraud Detection (FedDP-FD), a pri...

A graph neural network-based system for evaluating dance training effectiveness and dynamically adjusting training plans

The integration of digital technologies in dance education provides opportunities to enhance training effectiveness. Traditional motion-based systems primarily focus on static recognition and fail to model complex movement relationships or adapt training plans in real time. This research aims to develop an intelligent...

Design and Optimization of Graphene-Based Dual-Band Magneto–Electric Dielectric Resonator Multiple-Input Multiple-Output Antenna for 6G

Graph Neural Networks for Network Intrusion Detection: A Systematic Review of Contrastive, Explainable, and OOD-Robust Architectures

Autonomous AI-Driven Security Operations Centers (Autonomous SOC): Agentic Orchestration, Real-Time Incident Response, and Multi-Agent Defensive Playbooks

Abstract—Contemporary enterprise Security Operations Centers (SOCs) are on the brink of systemic collapse driven by three converging factors: exponential alert fatigue (>25,000 uninvestigated alerts daily per enterprise), acute global cybersecurity workforce shortages, and the emergence of automated, machine-speed cybe...

An event sentiment and herding-based stock price forecasting using graph neural networks

Similarity-Guided Graph Learning with Embedding-Space Oversampling for Anomaly Detection in Heterophilic Cryptocurrency Transaction Networks

The rapid development of cryptocurrency has led to an urgent demand for anomaly detection targeting illegal transaction activities. Cryptocurrency transaction networks have two core characteristics: class imbalance and connection heterophily. Existing anomaly-detection methods tend to cause over-smoothing of anomaly no...

zeshanalvi/HAST-GNN-Multi-Task-Crime-Prediction-using-Spatio-Temporal-Graph-Neural-Networks: HAST-GNN-Multi-Task-Crime-Prediction-using-Spatio-Temporal-Graph-Neural-Networks

Initial version of the code for HAST-GNN-Multi-Task-Crime-Prediction-using-Spatio-Temporal-Graph-Neural-Networks.

Sparsity-aware social recommendation via information graph bottleneck

With the widespread adoption of online social networks, social recommender systems have become a key paradigm for mitigating data sparsity and cold-start issues in traditional collaborative filtering. However, existing graph neural network (GNN) based recommendation models face two fundamental structural bottlenecks wh...

Diffusion Models Beyond Computer Vision: Tabular Data Synthesis, Graph Generation, and Time-Series Forecasting

Abstract—Denoising Diffusion Probabilistic Models (DDPMs) and Score-Based Generative Models have fundamentally transformed continuous image generation and multi-modal synthesis. However, their mathematical extension beyond Euclidean continuous pixel grids into structured enterprise domains—specifically heterogeneous ta...

FusionEP: Adaptive Cross-Modal Deep Fusion Network for Essential Protein Identification via Multi-Omics Integration and Contrastive Learning

Identifying essential proteins lays a foundation for dissecting disease mechanisms and mining therapeutic targets. Existing algorithms mostly adopt unimodal inputs and struggle to capture multi-level biological interactions from genomics, subcellular localization and proteomics. We present FusionEP, which is an adaptiv...

Beyond pairwise interactions: Accurate forecasting of co-evolutionary spreading on higher-order networks

Many co-evolutionary spreading processes involve higher-order interactions, yet the consequences of approximating them through pairwise representations remain insufficiently understood. Here, we uncover distinctive prediction failures of pairwise graph neural networks during node-level one-step conditional transition-p...