Search Results - Dynamic graph conventional autoencoder

ADG-KAE: novel adaptive dynamic graph embedded kernel autoencoder algorithm for industrial process monitoring and fault detection

Authors: Ruining Tong Zhiyu Xu Haini Qu et al.

Source: Measurement Science and Technology. 36:066212

PT-TDGCN: Pre-Trained Trend-Aware Dynamic Graph Convolutional Network for Traffic Flow Prediction.

Authors: Yang, Hanqing Wei, Sen Wang, Yuanqing

Source: Sensors (14248220); Nov2025, Vol. 25 Issue 21, p6709, 21p

Subject Terms: GRAPH neural networks, SPATIOTEMPORAL processes, TRAFFIC estimation, TEMPORAL databases, ARTIFICIAL neural networks

Ultra-Short-Term Photovoltaic Cluster Power Prediction Based on Photovoltaic Cluster Dynamic Clustering and Spatiotemporal Heterogeneous Dynamic Graph Modeling.

Authors: Liu, Yingjie Yang, Mao

Source: Electronics (2079-9292); Sep2025, Vol. 14 Issue 18, p3641, 27p

Subject Terms: PHOTOVOLTAIC power generation, SPATIOTEMPORAL processes, ARTIFICIAL neural networks, ELECTRIC power distribution, CLUSTER analysis (Statistics), ENERGY consumption forecasting

Detecting Anomalies in Dynamic Graphs via Memory enhanced Normality

Authors: Liu, Jie Shang, Xuequn Han, Xiaolin et al.

Index Terms: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, text

URL: http://arxiv.org/abs/2403.09039

HRRPGraphNet++: Dynamic Graph Neural Network with Meta-Learning for Few-Shot HRRP Radar Target Recognition.

Authors: Chen, Lingfeng Pan, Zhiliang Liu, Qi et al.

Source: Remote Sensing; Jun2025, Vol. 17 Issue 12, p2108, 24p

Subject Terms: GRAPH neural networks, AUTOMATIC target recognition, RADAR targets, REPRESENTATIONS of graphs, RADAR

DLG–IDS: Dynamic Graph and LLM–Semantic Enhanced Spatiotemporal GNN for Lightweight Intrusion Detection in Industrial Control Systems.

Authors: Liu, Junyi Wang, Jiarong Yan, Tian et al.

Source: Electronics (2079-9292); Oct2025, Vol. 14 Issue 19, p3952, 32p

Subject Terms: INTRUSION detection systems (Computer security), GRAPH neural networks, INTERNET security, INDUSTRIAL controls manufacturing, TIME-varying networks, MACHINE learning

Spatio-temporal graph convolutional autoencoder for transonic wing pressure distribution forecasting.

Authors: Immordino, Gabriele Vaiuso, Andrea Da Ronch, Andrea et al.

Source: Aerospace Science & Technology. Oct2025, Vol. 165, pN.PAG-N.PAG. 1p.

Subject Terms: *AUTOENCODERS, *TRANSONIC aerodynamics, *NONLINEAR mechanics, *GRAPH neural networks, *SPATIOTEMPORAL processes, *AERODYNAMIC load, *FORECASTING

DAHG: A Dynamic Augmented Heterogeneous Graph Framework for Precipitation Forecasting with Incomplete Data.

Authors: Tang, Hailiang Yang, Hyunho Zhang, Wenxiao

Source: Information; Nov2025, Vol. 16 Issue 11, p946, 24p

Subject Terms: PRECIPITATION forecasting, MISSING data (Statistics), ENVIRONMENTAL sciences, RECURRENT neural networks, TIME-varying networks, MACHINE learning, DATA structures, REINFORCEMENT learning

Variational Graph Convolutional Networks for Dynamic Graph Representation Learning

Authors: Aabid A. Mir Megat F. Zuhairi Shahrulniza Musa et al.

Source: IEEE Access, Vol 12, Pp 161697-161717 (2024)

Subject Terms: network security, deep learning, graph neural networks, Anomaly detection, complex networks, Electrical engineering. Electronics. Nuclear engineering, heterogeneous networks, TK1-9971

Access URL: https://doaj.org/article/205888aa585544ec8d0e7f0c49e14058

Intelligent fault prediction and diagnosis for wind-powered heating systems using graph neural networks.

Authors: Wang, Yuechao Zhao, Jizhong Tang, Donglai et al.

Source: Scientific Reports; 11/7/2025, Vol. 15 Issue 1, p1-16, 16p

Subject Terms: GRAPH neural networks, MULTISENSOR data fusion, FAILURE analysis, SPATIOTEMPORAL processes, RENEWABLE energy sources, REAL-time computing, MACHINE learning, DIAGNOSIS

Geographic Terms: CHINA

Dual-Branch Reconstruction Pre-Training with Multi-Layer Supervision and Regional Dynamic Graph Convolution for 2D Tooth Segmentation.

Authors: Wang, Haibin Liu, Li

Source: Sensing & Imaging; 10/18/2025, Vol. 26 Issue 1, p1-14, 14p

GATE: Graph Attention Neural Networks with Real-Time Edge Construction for Robust Indoor Localization using Mobile Embedded Devices.

Authors: Gufran, Danish Pasricha, Sudeep

Source: ACM Transactions on Embedded Computing Systems; 2025 Suppl 5, Vol. 24, p1-24, 24p

Subject Terms: INDOOR positioning systems, GRAPH neural networks, DEEP learning, REPRESENTATIONS of graphs, SMART devices

Adaptive dynamic graphs for anomaly detection via inter- and intra-diffusion.

Authors: Yuan, Ziqi Zhou, Haoyi Sun, Qingyun

Source: International Journal of Machine Learning & Cybernetics; Sep2025, Vol. 16 Issue 9, p6647-6668, 22p

Towards Predicting Urban Land Use Changes: A Dynamic Graph Alignment Perspective.

Authors: FAN, YU LU, XINJIANG LIU, HAO et al.

Source: ACM Transactions on Intelligent Systems & Technology; Apr2025, Vol. 16 Issue 2, p1-24, 24p

Subject Terms: GRAPH neural networks, URBAN land use, LAND use, CAUSAL inference, ACTIVITIES of daily living

MST-DGCN: Multi-Scale Temporal–Dynamic Graph Convolutional with Orthogonal Gate for Imbalanced Multi-Label ECG Arrhythmia Classification.

Authors: Chen, Jie Jiang, Mingfeng He, Xiaoyu et al.

Source: AI; Sep2025, Vol. 6 Issue 9, p219, 20p

Subject Terms: ARRHYTHMIA, CLASSIFICATION, ELECTROCARDIOGRAPHY, GRAPH neural networks

Optimizing the efficiency of perovskite solar cells for improved performance and energy conversion using temporal dynamic graph neural network.

Authors: Subha, T. D. Subash, T. D. Lalitha, S. D. et al.

Source: Journal of Computational Electronics; Oct2025, Vol. 24 Issue 5, p1-12, 12p

Graph-Variational Convolutional Autoencoder-Based Fault Detection and Diagnosis for Photovoltaic Arrays.

Authors: Arifeen, Murshedul Petrovski, Andrei Hasan, Md Junayed et al.

Source: Machines; Dec2024, Vol. 12 Issue 12, p894, 15p

Subject Terms: SOLAR cells, RENEWABLE energy sources, PHOTOVOLTAIC power systems, SOLAR energy, AUTOENCODERS

Multi dynamic temporal representation graph convolutional network for traffic flow prediction.

Authors: Wu, Zuojun Liu, Xiaojun Zhang, Xiaoling

Source: Nature Communications; 5/14/2025, Vol. 16 Issue 1, p1-13, 13p

GNPNM: A graph neural pore network model for predicting quasi-static drainage displacement patterns.

Authors: Kim, Yejin Suh, Hyoung Suk

Source: Computers & Geotechnics. Nov2025, Vol. 187, pN.PAG-N.PAG. 1p.

Subject Terms: *GRAPH neural networks, *FLUID dynamics, *CAPILLARY flow, *MACHINE learning, *ELECTRIC network topology, *PREDICTION models

DIGNN-A: Real-Time Network Intrusion Detection with Integrated Neural Networks Based on Dynamic Graph.

Authors: Liu, Jizhao Guo, Minghao

Source: Computers, Materials & Continua; 2025, Vol. 82 Issue 1, p817-842, 26p

Subject Terms: GRAPH neural networks, INFORMATION technology, COMPUTER network traffic, REPRESENTATIONS of graphs, COMPUTER network security