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graph neural networks

2,280 papers

#graph neural networks Open access Sep 2026

A novel data-level prompt injection attack against graph prompt learning

Graph Neural Networks (GNNs) are increasingly deployed in security-critical applications, including fraud detection, recommendation integrity analysis, and scientific knowledge mining. Recently, Graph Prompt Learning (GPL) has emerged as a parameter-efficient paradigm for adapting pretrained GNNs to downstream tasks. B...

Meng-Ying Yuan, Zhi-Yong Zhang, Gao-Yuan Quan et al. · 0 citations
#graph neural networks Dataset Open access Sep 2026

MHGNN-M-v1.0

A multi-source heterogeneous graph neural network for traffic congestion prediction with missing data

Weihua Huan · 0 citations
#graph neural networks Open access Sep 2026

Design of an Integrated Modern Approach to Detect and Prevent Data Poisoning Attacks in AI Systems: A Multi-Stage Defense Framework for Robust and Secure Learning

Adversarial threats like data poisoning attacks affect the training datasets and create biased, worse, or malicious AI models. Static heuristics and high false positive rates have impeded traditional defenses against such adaptive stealthy attacks. These defenses also do not generalize well in federated and non-IID set...

Nitesh L. Hatwar, V. K. Sharma, Bhushan Manjre · 0 citations
#graph neural networks Open access Sep 2026

OISES-Graph-Surrogate: geometry-aware surrogate modelling and inverse design of origami-inspired super-expandable scaffolds

Virtual laboratory and graph neural surrogate for origami-inspired super-expandable scaffolds in distraction osteogenesis: co-rotational beam and pore-scale Stokes solvers, an STL-to-graph front end, a multi-task heteroscedastic Scaffold Graph Network, parametric baselines, and the optimisation, ablation and repetition...

Sagor Das, Md. Tamzid Islam, Sanzida Afrin et al. · 0 citations
#graph neural networks Open access Sep 2026

OISES-Graph-Surrogate: geometry-aware surrogate modelling and inverse design of origami-inspired super-expandable scaffolds

Virtual laboratory and graph neural surrogate for origami-inspired super-expandable scaffolds in distraction osteogenesis: co-rotational beam and pore-scale Stokes solvers, an STL-to-graph front end, a multi-task heteroscedastic Scaffold Graph Network, parametric baselines, and the optimisation, ablation and repetition...

Sagor Das, Md. Tamzid Islam, Sanzida Afrin et al. · 0 citations
#graph neural networks Dataset Open access Sep 2026

MHGNN-M-v1.0

A multi-source heterogeneous graph neural network for traffic congestion prediction with missing data

Weihua Huan · 0 citations
#graph neural networks Open access Sep 2026

Partial differential equations in the age of machine learning: a critical synthesis of classical, machine learning, and hybrid methods

Abstract Partial differential equations (PDEs) govern physical phenomena across the full range of scientific scales, yet their computational solution remains one of the defining challenges of modern science. This critical review examines two mature but epistemologically distinct paradigms for PDE solution, classical nu...

Mohammad Nooraiepour, Jakub Wiktor Both, Teeratorn Kadeethum et al. · 1 citation
#reinforcement learning Dec 2026

A battery life prediction method based on graph neural network assisted by deep reinforcement learning

With the wide utilization of new energy batteries, the accurate prediction of battery life has become an urgent technical issue that needs to be addressed. Studies have developed battery life prediction models based on correlation analysis for feature extraction. This study introduces a DQN-based graph structure genera...

Han-Zhi Shen, Feng-Lian Li, Jian-Li Shao et al. · 0 citations
#artificial intelligence Open access Sep 2026

Improvement of personalized recommendation service level of intelligent libraries based on artificial intelligence and big data mining technology

The research conclusion confirms that the proposed model provides an advanced technical solution with both theoretical innovation and practical value for constructing an efficient and accurate personalized recommendation system for intelligent libraries of colleges and universities.

Chao Zhang · 0 citations
#graph neural networks Oct 2026

Enhancing Graph Neural Network Explainers Using a Distribution Shift Consistency-Guided Generator

Graph Neural Network (GNN) explainers aim to identify explanatory subgraphs that provide rationales for GNNs’ predictions. Nevertheless, the distribution shift of subgraphs introduces out-of-distribution (OOD) issues into GNNs’ predictions. The OOD issues compromise explainers’ optimization, as the optimization relies...

Guang-Yong He, Li Liu, You-Min Zhang et al. · 0 citations
#graph neural networks Open access Sep 2026

Biomimetic Spiking Neural Graph Dynamics: Neuromorphic Memory Consolidation and Neurotransmitter Modulation in Autonomous Cognitive Operating Systems

Modern autonomous agents predominantly manage state and memory through static vector embeddings and flat nearest-neighbor (k-NN) retrieval mechanisms. While effective for isolated semantic lookups, this mechanical paradigm suffers from severe cognitive pathologies in long-horizon reasoning tasks: lack of temporal recen...

Hai Nguyen · 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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