graph neural networks

Artificial Intelligence Reveals How Alloying Amount Turns Electronic Structure into Mechanical Performance

This repository provides the complete computational workflow for AlloyGCN-based prediction, surrogate modeling, and explainable analysis of Cantor alloys with additional alloying elements. The pipeline includes: Graph neural network (AlloyGCN) training and evaluation for process-dependent mechanical property prediction...

Graph neural networks guide progressive metric updates for congestion free OSPF reconfiguration

Graph Neural Network Guided Progressive Metric Updates (GNN-PMU), a graph-aware scheduler that ranks metric changes using topology, utilization, queue, capacity, and pending-update information, is presented.

Spectral network determinants of seizure-like synchronization and spread in coupled Hindmarsh–Rose brain models

An intelligent cybersecurity framework for banking systems: integrating neural networks, large language models, and federated learning for anti-money laundering and fraud detection

The fraud of money laundering costs the global financial system USD 800 billion to USD 2 trillion annually, while digital banking contributes to the increasing number and complexity of money laundering transactions. If there are adversarial forces that are constantly adapting their approach to avoid complying with a co...

Synthetic false data injection-inspired perturbation framework for power grid attack detection using graph neural networks and deep learning models

The novelty of this work lies in providing a unified, leakage-free comparative framework spanning graph- and non-graph-based deep learning architectures under identical experimental conditions, which provides a realistic and extensible benchmark for future power-grid cybersecurity research.

UltraGNN: A Sparse-Operator-Aware Framework for Accelerating Graph Neural Networks on Tensor Cores

Graph Neural Networks (GNNs) have achieved widespread success from social networks to AI-for-Science. Most existing GNN frameworks adopt scatter-first (edge-centric) or gather-first (vertex-centric) scheduling paradigms for message passing. However, these paradigms are closely tied to traditional CUDA-core execution mo...

Rule anchored graph learning improves synthetic identity fraud detection through multi relation structure rather than score level fusion

Synthetic Identity Fraud (SIF), in which fraudsters combine real and fabricated information to create untraceable identities, is a fast-growing and costly problem in payment systems. Prior work treats graph neural networks as an assumed improvement over transparent rule-based identity-linkage scoring, but rarely tests...

Interpretable Multimodal Engagement Prediction with Graph-based Mixture-of-Experts

Automatic engagement prediction is a significant aspect of Human-Computer Interaction (HCI) and affective computing, enabling systems to capture user interest and deliver timely interventions. In dyadic conversations, a person’s (target’s) engagement can be influenced by both their own behavioral signals and those of t...

Community detection via graph attention auto-encoders and decoders with non-negative matrix factorization

Edge-Adaptive Dynamic Spatiotemporal Graph Learning for Efficient Mobile Traffic Prediction in AIoT Cellular Networks

AIモデルの蒸留・盗用問題と量子実用化(Q-day)による知的所有権の再構築に関する総合調査報告 (英語副題: Comprehensive Investigation on AI Model Distillation, IP Theft, and Post-Q-Day Intellectual Property Restructuring)

日本語概要 (Japanese Abstract) 本調査報告書は、2025年末から2026年にかけて激化した最先端AIフロンティアモデルに対する「産業規模の敵対的蒸留攻撃(Adversarial Distillation Attacks)」の実態を網羅的に分析し、目前に迫る量子コンピューティングの実用化(Q-day)がAIの知的所有権(IP)、モデルの透明性、および産業構造に与える不可逆的な地...

Adaptive Graph Sparsification for Efficient Graph Neural Networks

This preprint presents an experimental study of graph sparsification for graph neural networks. The study evaluates multiple edge-selection strategies, including random, degree-based, structural, feature-based, and multi-criteria sparsification, across the Cora, CiteSeer, PubMed, Chameleon, and Squirrel datasets. The e...