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graph neural networks

1,934 papers

#graph neural networks Sep 2026

Spatio-temporal graph neural network for state awareness and fault diagnosis in virtual power plant communication networks

Power communication networks in virtual power plants (VPPs) play a critical role in enabling reliable information exchange among distributed energy resources, energy storage systems, controllable loads, and dispatch centers. However, their complex topology, strong inter-node dependency, and hidden fault propagation pat...

Ting-Ting Ren, Cai-Xiao Ouyang, Li Yang et al. · 0 citations
#graph neural networks Open access Sep 2026

Waveform-Prediction Augmentation and Deep Manifold Learning Enable Imbalanced Fault Diagnosis in Rotating Machinery

Fault diagnosis of rotating machinery is essential for ensuring the reliable and safe operation of industrial equipment. However, imbalanced training data often bias intelligent diagnostic models toward majority classes, resulting in insufficient representation of minority faults. In addition, the high dimensionality a...

Yu Tian, Shun-Sheng Guo, Yibing Li et al. · 0 citations
#graph neural networks Sep 2026

Uncertainty Quantification for Physics-Informed Traffic Graph Networks with Adversarial Attacks

This paper integrates multiple UQ baseline methods with physics-informed modeling to systematically investigate how physical constraints influence uncertainty estimation in traffic forecasting and proposes a physics-informed loss function that enhances the model's ability to capture physically consistent dynamics and i...

Tianshu Bao, D. T. Nguyen, Xia-Oou Liu et al. · 0 citations
#graph neural networks Open access Sep 2026

KEAP1-NRF2 Computational Discovery

Reproducible computational workflow for the identification and prioritization of putative non-covalent KEAP1-NRF2 protein-protein interaction inhibitors. The workflow integrates structure-based virtual screening, binding-pocket analysis, molecular docking, MM-GBSA rescoring, molecular dynamics simulations, transfer-lea...

Umair Wahedi · 0 citations
#graph neural networks Open access Sep 2026

High-fidelity 3D reconstruction of navel oranges using 3D Gaussian splatting

Abstract High-precision 3D reconstruction is essential for assessing external quality and extracting phenotypic parameters of navel oranges. Conventional Structure-from-Motion (SfM) pipelines struggle with the weakly textured characteristics and highly specular surfaces of navel oranges, resulting in incomplete reconst...

Huamao Zhou, Chao Zhou, Wenjie Zhong et al. · 0 citations
#graph neural networks Open access Sep 2026

Machine Learning-Assisted Optimization of CMOS VLSI Circuits for Low-Power and High-Speed Applications

Scaling of complementary metal-oxide-semiconductor (CMOS) technology into the deep-nanometre regime has made the simultaneous minimisation of power and propagation delay one of the hardest problems in very-large-scale integration (VLSI) design. Supply voltage, threshold-voltage assignment, transistor sizing, body biasi...

Ankit Soni · 0 citations
#artificial intelligence Open access Sep 2026

Messages Passed Along the Edges: A Interdisciplinary Mapping Review of Graph Neural Networks for Relational Data

This article presents a narrative review of Graph Neural Networks for Relational Data in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an interpretive...

Zen Revista, 10 IA · 0 citations
#graph neural networks Open access Sep 2026

A SURVEY OF DEEP LEARNING ARCHITECTURES FOR TRANSACTION AND FINANCIAL FRAUD DETECTION, ANCHORED ON THE MULTI-TASK CNN BEHAVIOURAL EMBEDDING MODEL

This survey reviews deep learning architectures for transaction and financial fraud detection published strictly after 2024, using the Multi-task CNN Behavioural Embedding Model (MTCNN) proposed by Qu et al.[1] in 2024 as a conceptual anchor rather than as one of the post-2024 studies under review. MTCNN's core ideas —...

Dr. S. Jeyalaksshmi2 Ms. S. Jayanthi1* · 0 citations
#graph neural networks Open access Sep 2026

A SURVEY OF DEEP LEARNING ARCHITECTURES FOR TRANSACTION AND FINANCIAL FRAUD DETECTION, ANCHORED ON THE MULTI-TASK CNN BEHAVIOURAL EMBEDDING MODEL

This survey reviews deep learning architectures for transaction and financial fraud detection published strictly after 2024, using the Multi-task CNN Behavioural Embedding Model (MTCNN) proposed by Qu et al.[1] in 2024 as a conceptual anchor rather than as one of the post-2024 studies under review. MTCNN's core ideas —...

Dr. S. Jeyalaksshmi2 Ms. S. Jayanthi1* · 0 citations
#artificial intelligence Open access Sep 2026

Messages Passed Along the Edges: A Interdisciplinary Mapping Review of Graph Neural Networks for Relational Data

This article presents a narrative review of Graph Neural Networks for Relational Data in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an interpretive...

Zen Revista, 10 IA · 0 citations
#graph neural networks Open access Sep 2026

AdaH-Chain: Deterministic capacity-aware GNNs sharding in heterogeneous blockchains

AdaH-Chain, a deterministic capacity-aware sharding framework that replaces unsafe floating-point operations with a symmetric fixed-point quantization mechanism to mathematically guarantee bit-wise consensus safety, is proposed and a Capacity-Aware GNN Sharding algorithm is introduced.

Zi-Yao Wang, Yong-Jiao Sun, Heng-Tai Zhao et al. · 0 citations
#artificial intelligence Open access Sep 2026

Consciousness Is a Phase Transition: A Two-Channel Thermodynamic Criterion and an Exclusion Principle

We propose that consciousness is a phase transition of a non-equilibrium dissipative structure, characterised by two independently controlled channels that must both be driven past threshold. The central result is an exclusion criterion: a macroscopic dissipative structure cannot be conscious unless it satisfies three...

FatJack · 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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