graph neural networks

2,002 papers

#graph neural networks Open access Sep 2026

A graph neural network-based framework for hierarchical time series forecasting in retail

Guoping Hu, C. Giurcăneanu, Qian Chang et al. · Knowledge-Based Systems · 1 citation

#graph neural networks Sep 2026

Multi-scale dynamic graph neural networks for response prediction and anomaly detection in ancient timber structures

Yuan Yao, Juan Wang, Na Yang · Structures · 0 citations

#graph neural networks Sep 2026

Malope: Memory-Aware and Locality-Preserved Graph Neural Network Training

Training GNNs on large-scale graphs imposes significant memory constraints for storing substantial amounts of graph structures and node features. This often necessitates the use of memory extensions such as SSDs, leading to a memory hierarchy with disparities in capacity and access speed. Existing approaches focus on m...

Junkun Shen, Yuezhi Che, Haoran Zhou et al. · IEEE Transactions on Paralle... · 0 citations

#graph neural networks Sep 2026

A spectral heterogeneous graph neural network with multi-sensor fusion for machine fault diagnosis

Jun Wang, Zhilin Dong, Weidong Jiao et al. · Engineering applications of... · 1 citation

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#graph neural networks Sep 2026

VulGNN: A high-fidelity graph neural network framework for robust smart contract vulnerability detection

Xinyi Chen, Weihua Bai, Jialing Zhao et al. · Advanced Engineering Informa... · 0 citations

#graph neural networks Sep 2026

EdgeSAC: Graph Neural Soft Actor-Critic for Hierarchical IoV Resource Management

Intelligent Transportation Systems (ITS) rely on the Internet of Vehicles (IoV) to sustain high data rates and low latency under dynamic and heterogeneous conditions. Joint power and spectrum control across macro and micro tiers remains challenging due to mobility, interference coupling, and large continuous action spa...

Arif Raza, Uddin Md. Borhan, Yueling Che et al. · IEEE Transactions on Mobile... · 0 citations

#graph neural networks Sep 2026

Sustainable energy management in IoT-enabled hybrid microgrids via graph neural network-optimized automatic generation control

N. Almolhis, Salahaldeen Duraibi · Sustainable Computing: Infor... · 0 citations

#graph neural networks Sep 2026

Automated generation and surrogate modeling of 3D urban wind simulation based on physics-informed graph neural networks: a two-stage framework

Sihao Li, Yangze Liang, Guangyao Chen et al. · Advanced Engineering Informa... · 0 citations

#graph neural networks Sep 2026

Construction supply-chain carbon footprint with graph neural network-based input-output framework

Hakpyeong Kim, Jun-Ki Choi, Taehoon Hong · Building and Environment · 0 citations

#graph neural networks Sep 2026

Information bottleneck-based correlation analysis with graph neural ODEs for network reconstruction

Bing Wang, Haowei Zhang, Yuexing Han · Chaos, Solitons & Fracta... · 0 citations

#graph neural networks Open access Sep 2026

Modeling adolescents' perception of cycling safety: A new approach using graph neural networks and street view imagery

Xiaobing Wei, Filip Biljecki, Pengyuan Liu et al. · Computers, Environment and U... · 0 citations

#graph neural networks Open access Sep 2026

Graph neural network-based multi-agent reinforcement learning for active voltage control: Performance and topology robustness

C. Eze, Chetan Prakash, A. Monti · Energy and AI · 0 citations

From tech blogs

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MIT News · Artificial Intelligence · news.mit.edu Oct 13, 2026

How artificial neural networks build their own learning machinery

Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.

MIT News · Artificial Intelligence · news.mit.edu Jul 15, 2026

3 Questions: Neural transparency and the future of AI design

Assistant Professor Pat Pataranutaporn describes a new interface that lets everyday users glimpse inside an AI's neural network before their chatbot ever says a word.

Microsoft Research Blog · microsoft.com 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.

MIT News · Artificial Intelligence · news.mit.edu Jul 6, 2026

graph neural networks

A graph neural network-based framework for hierarchical time series forecasting in retail

Multi-scale dynamic graph neural networks for response prediction and anomaly detection in ancient timber structures

Malope: Memory-Aware and Locality-Preserved Graph Neural Network Training

A spectral heterogeneous graph neural network with multi-sensor fusion for machine fault diagnosis

VulGNN: A high-fidelity graph neural network framework for robust smart contract vulnerability detection

EdgeSAC: Graph Neural Soft Actor-Critic for Hierarchical IoV Resource Management

Sustainable energy management in IoT-enabled hybrid microgrids via graph neural network-optimized automatic generation control

Automated generation and surrogate modeling of 3D urban wind simulation based on physics-informed graph neural networks: a two-stage framework

Construction supply-chain carbon footprint with graph neural network-based input-output framework

Information bottleneck-based correlation analysis with graph neural ODEs for network reconstruction

Modeling adolescents' perception of cycling safety: A new approach using graph neural networks and street view imagery

Graph neural network-based multi-agent reinforcement learning for active voltage control: Performance and topology robustness

From tech blogs

How artificial neural networks build their own learning machinery

3 Questions: Neural transparency and the future of AI design

Verifying Rust cryptography in SymCrypt, from standards to code

Toward a future that preserves benefits of neurotechnology for all

NeuralGCM harnesses AI to better simulate long-range global precipitation

Neural Super Sampling is here!