graph neural networks

Toward AI-Augmented Applied Systems: Emerging AI and LLM Directions Across Trajectory Prediction, Cybersecurity, and Engineering Automation

This paper reviews fifteen recent publications (2015-2026) spanning trajectory prediction, cybersecurity and trust management, classical machine learning and algorithmic optimisation, and engineering automation, and asks a forward-looking question: where is artificial intelligence — particularly Large Language Models (...

AI-Driven Drug Repurposing for Multidrug-Resistant Infections: Computational Rigor, Translational Realities, and Strategic Future Directions

Antimicrobial resistance (AMR) is a rapidly escalating global health crisis, and multidrug-resistant (MDR) bacterial pathogens cause substantial morbidity, mortality, and economic burden. Conventional antibiotic-discovery pipelines remain slow, costly, and prone to high attrition, creating an urgent need for alternativ...

Graph Neural Network

A spatio-temporal graph neural network model for predicting economic impact of sports events and analyzing regional synergy effects

Dual Graph Neural Networks for Social Bot Detection: Social Relation with an Auxiliary Behavior Similarity Graph

Social bots threaten the integrity of online social platforms by spreading misinformation, manipulating public opinion, and carrying out fraudulent activities. Detecting social bots is therefore not just a classification problem: looking ahead, a detector can also act as a discriminative component in agent-based social...

Compressing Recommender and Ranking Models for Edge Deployment: A Survey

Edge deployment of recommender and ranking models enables low-latency personalization, privacy-preserving inference, reduced network dependence, and context-aware decisions on mobile, embedded, and resource-constrained platforms. Yet contemporary recommenders combine massive embedding tables, interaction networks, sequ...

Dynamic causal graph representation learning for neurological disorders recognition

The effectiveness of dynamic brain networks in diagnosing neurological disorders, which represent a worldwide threat to human health, is increasingly recognized. Numerous Graph Neural Network (GNN) models have been created to autonomously detect aberrant integration patterns related to specific neurological conditions...

"Artificial Intelligence in Banking Fraud Detection: A Systematic Review using the Prisma Approach"

The rapid growth of digital banking, instant payments, mobile applications and card-not-present transactions has improved access to financial services while simultaneously widening the attack surface for fraud.Conventional rulebased systems remain indispensable for regulatory control, yet they are often unable to recog...

Persistent Node Evidence: An Investigation-Oriented Representation for Provenance-Based Intrusion Detection

Code and derived data for: Region-wise Parkinson's disease detection from resting-state EEG using multi-domain features and temporal graph neural networks.

This repository holds the code, derived data and trained model checkpoints for a study of region-wise Parkinson’s disease detection from resting-state EEG, using 17 multi-domain features and three temporal graph neural networks (TGCN, TGIN, TGAT). It includes the pipeline notebooks, the fixed subject-wise split, the wi...

Hardware-Enforced Confidential Computing and Homomorphic Encryption for Cross-Bank Financial Fraud AI

Abstract: Sophisticated financial crime syndicates orchestrate synthetic identity fraud, layering, and Authorized Push Payment (APP) laundering across dozens of correspondent banks and national jurisdictions within minutes. Single-institution machine learning models fail to detect these cross-bank topological patterns...

A Telecom Fraud Detection Method Based on Graph Attention Networks and Cost-Sensitive Learning