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graph neural networks

2,177 papers

#graph neural networks Book Oct 2026

EEG-Based Detection of Autism Spectrum Disorder Using Hypergraph Convolutional Networks with Explainable AI Interpretability

The occurrence of autism spectrum disorder (ASD) has been increasing in recent years, emphasizing the importance of Early diagnosis and intervention. Early detection provides a promising solution for ASD affected people to lead their life individually. Existing methods for ASD diagnosis face challenges in time complexi...

K. Lalli, M. Senbagavalli · 0 citations
#graph neural networks Open access Oct 2026

AI-Driven Hull Defect Evolution Prediction: A Hybrid Physics-Informed Deep Learning Framework for Maritime Structural Health Monitoring

Ship hulls operate under repeated wave impact, changing loading conditions, and prolonged exposure to seawater. Over years of service, these effects can produce corrosion, local pitting, and fatigue cracks in important structural members. In practice, hull condition is still assessed largely through scheduled drydock s...

Mrs.B.Moohambigai, S.Krishna Kumar, I.Sayed Arman, T.Thangaraj, S.Azizbasha · 0 citations
#graph neural networks Oct 2026

TradeGraph: Learning Global Semiconductor Market Dynamics from International Trade Networks using Hybrid Graph Neural Networks

The global semiconductor industry has become a strategic pillar of the digital economy and mainly supports the artificial intelligence technology, cloud computing, advanced manufacturing, and modern electronic devices. Forecasting international semiconductor trade flows is therefore essential for governments, researche...

Donalson WILSON, Salma Liichi, Chaimae Chouaa et al. · 0 citations
#graph neural networks Open access Oct 2026

Explainable spatiotemporal Graph Neural Networks: Multi-City PM2.5 forecasting in Bangladesh with geographic graph topology validation

Accurate forecasting of fine particulate matter (PM 2.5 ) is critical for mitigating severe health risks in South Asia, yet it remains challenging in data-scarce regions like Bangladesh. While spatiotemporal graph neural networks (GNNs) offer a promising solution by capturing atmospheric transport, their effectiveness...

Pritom Ray Nobin · 0 citations
#graph neural networks Open access Oct 2026

areaFluForecast: neighborhood-scale influenza forecasting for public health departments

A Python package that gives public health departments 1-4 week influenza forecasts for neighborhoods, ZIP codes or districts from their own weekly rates and area boundaries. It trains a spatio-temporal graph neural network, a tuned LSTM and Cola-GNN, checks each against persistence on the department's own past seasons...

Mateo Biggs · 0 citations
#graph neural networks Book Open access Oct 2026

ИНЖЕНЕРНАЯ АНТРОПОЛОГИЯ. АРХИТЕКТУРА ПЕРЕХОДА. СБОРНИК КНИГ VIII-XI

ИНЖЕНЕРНАЯ АНТРОПОЛОГИЯ. АРХИТЕКТУРА ПЕРЕХОДА Сборник книг VIII–XI Аннотация «Инженерная антропология. Архитектура перехода» объединяет четыре взаимосвязанные книги, развивающие междисциплинарную модель трансформации человеческих когнитивных, психологических и смыслообразующих систем. Центральный вопрос исследования —...

Oleg Kvashuk, Violetta Podets · 0 citations
#graph neural networks Oct 2026

Predictive Anomaly Detection and Self-Healing in Active Distribution Networks Using a Graph Neural Network Digital Twin

Active distribution networks (ADNs) are exposed to two anomaly classes that resist unified detection: physical line outages and cyber false-data-injection (FDI) attacks. The standard defense, weighted- least-squares (WLS) bad-data detection, catches FDI but is structurally blind to topology changes, because a reconfigu...

Aamir Nawaz, Zahid Javid, William Holderbaum · 0 citations
#graph neural networks Book Open access Oct 2026

Beyond Gravity Models: Predicting Human Migration with Spatial Gates and Graph Neural Networks

Internal migration shifts dominate demographic changes and place asymmetric strain on infrastructure and public services. The fragmented, institution-specific nature of Mexican municipal data has limited the study of large-scale dyadic modeling in Mexico. We make two contributions: first, we construct a dyadic dataset...

Pranav Verma, Noah Dengler, Angela C. Lyons et al. · 0 citations
#graph neural networks Oct 2026

Systematic Approach to Enhance Explainable Memory-Augmented Hybrid Graph Attention Framework for Intelligent Banking Risk Prediction

Intelligent prediction of banking risk has drawn more attention with the emergence of the digitization of financial systems due to increasing threats from financial fraud. Standard machine learning and deep learning frameworks have struggled to identify hidden relational dependencies and behavioral patterns in customer...

Anish Mary George, Sudha Seethambaram · 0 citations
#graph neural networks Review Open access Oct 2026

A Survey of Smart Contract Vulnerability Detection Based on Tree Representation

Smart contracts, characterized by automation, transparency, and decentralization, can establish efficient, secure, and reliable blockchain fund management and data processing mechanisms, playing a crucial supporting role in blockchain application scenarios. However, security issues in smart contracts are increasingly p...

Gong-Ming Wang, Jun-Song Zhou, Bing-Sen Li et al. · 0 citations
#graph neural networks Open access Oct 2026

Non-linear mechanical field reconstruction coupling recurrent neural networks with physics-informed graph neural networks

Reconstructing local stress fields in heterogeneous microstructures under non-linear, history-dependent loading remains a major computational bottleneck in multi-scale simulations. We propose a coupled LSTM-GNN framework that links the temporal and spatial aspects of local stress field reconstruction. A Long Short-Term...

Manuel Ricardo Guevara Garban, Yves Chemisky, Michaël Clément et al. · 0 citations
#graph neural networks Open access Oct 2026

Harnessing floating car data, traffic camera observations, and network flow analysis for traffic volume estimation

Cities increasingly rely on vehicle trajectory data to monitor traffic conditions; however, such data offer only a partial and spatially heterogeneous view of network dynamics and exhibit systematic biases across corridors and time periods. In contrast, surveillance cameras can provide high-fidelity traffic information...

Antonina Kosikova, Mehmet Kerem Türkcan, Ahmed Darrat et al. · 0 citations

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Microsoft Research Blog Jul 13, 2026

Verifying Rust cryptography in SymCrypt, from standards to code

Cryptographic code supports vital protections in modern computing systems. Learn how a new method helps verify code as developers write it while preserving speed and adaptability as it gets implemented and evolves. The post Verifying Rust cryptography in SymCrypt, from standards to code appeared first on Microsoft Research.

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