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graph neural networks

2,172 papers

#graph neural networks Open access Oct 2026

XGCN-GAT: An Explainable and Robustness-Aware Hybrid Graph Neural Network Framework for Android Malware Detection

The continued growth of the Android ecosystem has been accompanied by an escalating volume of malicious applications, motivating the development of detection methods that are simultaneously accurate, transparent, and resilient to evasion. Graph-based learning has emerged as a powerful paradigm for Android malware detec...

Marwa Mamdouh, Ahmed Elsheikh, Khaled Elsayed · 0 citations
#graph neural networks Oct 2026

Adaptive graph construction and graph contrastive learning for geospatial feature embedding

Geospatial features serve as a cornerstone in urban planning and location-based services. Effective geospatial feature embedding is crucial for downstream geospatial Deep Neural Networks (DNN) applications. Currently, geospatial feature representation learning approaches rely on image or textual data to implicitly enco...

Tan Yongbin, Jun Wu, Rongfeng Cai et al. · 0 citations
#graph neural networks Open access Oct 2026

Adaptive deanonymization of blockchain transactions leveraging integrative neural architectures in decentralized networks

Abstract The pseudonymous nature of decentralized networks like Bitcoin presents tremendous obstacles for regulatory compliance, forensic auditing, and cybercrime investigation. Traditional heuristics and single-modality machine learning (ML) frameworks provide partial transparency but usually suffer from the modality...

Rohit Saxena, Brijesh Kumar Chaurasia, Abhinav Awasthi et al. · 0 citations
#graph neural networks Open access Oct 2026

Machine Learning Techniques for Malware Detection

Malware is one of the major threats to modern computer systems, mobile devices, cloud platforms, and networks. Traditional signature-based antivirus systems are effective against known malware but often have difficulty detecting new, polymorphic, obfuscated, and zero-day variants. Machine Learning (ML) provides a data-...

Dr. S. B. Sarvaiya, Tanvi Kukadkar, Achal Bhelkar · 0 citations
#graph neural networks Dataset Open access Oct 2026

DE SpotNet: Direct Electron neural networks for diffraction spot detection and center refinement in 4D-STEM

Trained weights for DE SpotNet, Direct Electron's neural-network diffraction-spot (Bragg disk) detector, and its two center-refinement networks, DE CenterNet and DE FriedelNet, for 4D-STEM and scanning precession electron diffraction. Used by SpyDE, pyxem and quantem. License: CC BY-NC 4.0. Commercial use (for example,...

Carter Francis · 0 citations
#graph neural networks Open access Oct 2026

Research on the prediction of serviceability of mountain bridges based on the Graph-Guided and Deep Attention Network model

China’s infrastructure has transitioned from rapid construction to long-term operation, making accurate prediction of bridge service behavior critical for structural safety. The 2024 Zhashui bridge collapse underscores the urgent need for precise predictive models to mitigate catastrophic consequences. Conventional pre...

Cheng Wang, Changlong Zhao, Hanmin Pei et al. · 0 citations
#graph neural networks Open access Oct 2026

Spatial Statistics for Screening Molecular Structures

ABSTRACT The dominant paradigm in computational materials discovery relies on heavily parameterized deep architectures, including message‐passing graph and equivariant networks, that typically require large density functional theory (DFT)‐labeled training sets and produce nonconvex latent representations complicating i...

Pranoy Ray, Surya R. Kalidindi · 0 citations
#graph neural networks Open access Oct 2026

Graph neural networks for transmission grid topology control: Busbar information asymmetry and heterogeneous representations

Factors such as the proliferation of renewable energy and electrification contribute to grid congestion which is a pressing problem. Topology control is an appealing method for relieving congestion, but traditional approaches for topology discovery have proven too slow for practical applications. Recent research has fo...

Matthijs de Jong, Jan Viebahn, Yuliya Shapovalova · 0 citations
#graph neural networks Oct 2026

Elucidating Lithium-Ion Dynamics in Highly Concentrated Electrolytes with Markov State Models

Highly concentrated electrolytes (HCEs) have attracted considerable attention for lithium batteries owing to their wide electrochemical stability windows and favorable interfacial properties, which originate from their anion-rich solvation structures. The dynamics of lithium ions play a key role for HCEs performance;...

Tao Wang, Tian-Ying Yan · 0 citations
#reinforcement learning Oct 2026

Closed-Loop, Event-Driven Machine Learning for Autonomous Triage of Single-Photon Emitters

Abstract Identifying which candidate sites on a solid-state sample are single-photon emitters requires a Hanbury Brown–Twiss (HBT) measurement of the second-order correlation function g(2)(τ), which is slow because photon coincidences accumulate at a rate proportional to the square of the detected count rate. We examin...

Tanvir M. Mahim, Md Nahin Islam, M. Mosaddequr Rahman et al. · 0 citations
#reinforcement learning Open access Oct 2026

A Hybrid Graph Neural Network–Deep Reinforcement Learning Framework for Robust Multi-Modal Sensor Fusion and Real-Time Dynamic Obstacle Detection in Autonomous Environments

The research presents an intelligent framework for autonomous systems that performs multimodal sensor fusion to improve the identification of obstacles operating in dynamic scenarios.The proposed solution allows robots to handle complex and messy situations.The system leverages graph neural networks to capture spatial...

Unknown authors · 0 citations
#artificial intelligence Open access Oct 2026

Design Space Exploration of Reinforcement Learning Configurations for Post-Legalization Placement

Graph Neural Networks (GNNs) and Reinforcement Learning (RL) have recently been explored for VLSI placement optimization, but their effectiveness for post-legalization placement refinement remains unclear. This paper presents a systematic design space exploration of a GNN-Proximal Policy Optimization (GNN-PPO) framewor...

Foteini Oikonomou, Maria Pantazi-Kypraiou, George Goudroumanis 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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