Výsledky vyhledávání - (dynamic OR dynamika) graph convolutional autoencoder*

Drug-target interaction prediction based on graph convolutional autoencoder with dynamic weighting residual GCN

Autoři: Ming Zeng Min Wang Fuqiang Xie a další

Zdroj: BMC Bioinformatics, Vol 26, Iss 1, Pp 1-29 (2025)

Témata: Drug-target interaction, Dynamic weighting convolutional residual connection, Dual self-supervised joint training mechanism, Graph convolutional autoencoder, Generative adversarial network, Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5

Popis souboru: electronic resource

Relation: https://doaj.org/toc/1471-2105

Přístupová URL adresa: https://doaj.org/article/c9b441325e6d409dbc981ebfca8af0c6

Dynamic Causal Explanation Based Diffusion-Variational Graph Neural Network for Spatiotemporal Forecasting

Autoři: Liang, Guojun, Postdoktor Tiwari, Prayag Nowaczyk, Sławomir a další

Zdroj: IEEE Transactions on Neural Networks and Learning Systems. 33(5):9524-9537

Témata: Diffusion process, graph neural networks (GNNs), spatiotemporal forecasting, variational graph autoencoders (VGAEs)

Popis souboru: print

Přístupová URL adresa: https://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-55718
https://doi.org/10.1109/tnnls.2024.3415149

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

Autoři: Yang, Hanqing Wei, Sen Wang, Yuanqing

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

Témata: *GRAPH neural networks, *SPATIOTEMPORAL processes, *TRAFFIC estimation, *TEMPORAL databases, *ARTIFICIAL neural networks

Drug-target interaction prediction based on graph convolutional autoencoder with dynamic weighting residual GCN.

Autoři: Zeng, Ming Wang, Min Xie, Fuqiang a další

Zdroj: BMC Bioinformatics; 7/29/2025, Vol. 26 Issue 1, p1-29, 29p

Témata: DRUG discovery, GRAPH neural networks, DRUG receptors, MACHINE learning

A hybrid adversarial autoencoder-graph network model with dynamic fusion for robust scRNA-seq clustering

Autoři: Binhua Tang Yingying Feng Xinyu Gao

Zdroj: BMC Genomics, Vol 26, Iss 1, Pp 1-13 (2025)

Témata: ScRNA-seq, Self-supervised clustering, GCN, Adversarial AE, Cross-attention mechanism, Biotechnology, TP248.13-248.65, Genetics, QH426-470

Popis souboru: electronic resource

Relation: https://doaj.org/toc/1471-2164

Přístupová URL adresa: https://doaj.org/article/2a5ea00c188941fbb66094bbf69d63c0

Enhanced Variational Graph Convolutional Networks with Multi-Scale Convolutions and Attention Mechanisms for Dynamic Network Analysis

Autoři: Aabid Ahmad Mir Megat F. Zuhairi Shahrulniza Musa a další

Zdroj: Engineering, Technology & Applied Science Research. 15:19838-19847

A hybrid adversarial autoencoder-graph network model with dynamic fusion for robust scRNA-seq clustering.

Autoři: Tang, Binhua Feng, Yingying Gao, Xinyu

Zdroj: BMC Genomics; 8/18/2025, Vol. 26 Issue 1, p1-13, 13p

Témata: CLUSTERING algorithms, GRAPH neural networks, RNA sequencing, GENERATIVE adversarial networks, DATA integration, DEEP learning, BIOINFORMATICS, CELL differentiation

CasDacGCN: A Dynamic Attention-Calibrated Graph Convolutional Network for Information Popularity Prediction.

Autoři: Zhang, Bofeng Zhu, Yanlin Zhang, Zhirong a další

Zdroj: Entropy; Oct2025, Vol. 27 Issue 10, p1064, 18p

Témata: SOCIAL network analysis, GRAPH neural networks, INFORMATION dissemination, CASCADES (Fluid dynamics)

Enhanced Variational Graph Convolutional Networks with Multi-Scale Convolutions and Attention Mechanisms for Dynamic Network Analysis.

Autoři: Mir, Aabid Ahmad Zuhairi, Megat F. Musa, Shahrulniza a další

Zdroj: Engineering, Technology & Applied Science Research; Feb2025, Vol. 15 Issue 1, p19838-19847, 10p

Témata: GRAPH neural networks, DEEP learning, CYBERTERRORISM, NEIGHBORHOODS, CLASSIFICATION

Variational Graph Convolutional Networks for Dynamic Graph Representation Learning

Autoři: Aabid A. Mir Megat F. Zuhairi Shahrulniza Musa a další

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

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

Přístupová URL adresa: https://doaj.org/article/205888aa585544ec8d0e7f0c49e14058

Seismic damage identification by graph convolutional autoencoder using adjacency matrix based on structural modes

Autoři: Minkyu Kim Junho Song

Zdroj: Earthquake Engineering & Structural Dynamics. 53:815-837

Témata: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 0201 civil engineering

Analysis of China's High-Speed Railway Network Using Complex Network Theory and Graph Convolutional Networks.

Autoři: Xu, Zhenguo Li, Jun Moulitsas, Irene a další

Zdroj: Big Data & Cognitive Computing; Apr2025, Vol. 9 Issue 4, p101, 21p

Témata: HIGH speed trains, GRAPH theory, AUTOENCODERS, JOINT use of railroad facilities, CITIES & towns

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

Autoři: Immordino, Gabriele Vaiuso, Andrea Da Ronch, Andrea a další

Popis souboru: text

Relation: https://eprints.soton.ac.uk/504263/1/1-s2.0-S1270963825005875-main.pdf; Immordino, Gabriele, Vaiuso, Andrea, Da Ronch, Andrea and Righi, Marcello (2025) Spatio-temporal graph convolutional autoencoder for transonic wing pressure distribution forecasting. Aerospace Science and Technology, 165, [110516]. (doi:10.1016/j.ast.2025.110516 ).

Dostupnost: https://eprints.soton.ac.uk/504263/

Predicting Transonic Flowfields in Non-Homogeneous Unstructured Grids Using Autoencoder Graph Convolutional Networks

Autoři: Immordino, Gabriele Vaiuso, Andrea Da Ronch, Andrea a další

Témata: Computer Science - Computational Engineering, Finance, and Science, Computer Science - Machine Learning, text

URL: http://arxiv.org/abs/2405.04396

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

Autoři: Chen, Jie Jiang, Mingfeng He, Xiaoyu a další

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

Témata: ARRHYTHMIA, CLASSIFICATION, ELECTROCARDIOGRAPHY, GRAPH neural networks

Predicting transonic flowfields in non–homogeneous unstructured grids using autoencoder graph convolutional networks

Autoři: Immordino, Gabriele Vaiuso, Andrea Da Ronch, Andrea a další

Popis souboru: text

Relation: https://eprints.soton.ac.uk/504234/1/1-s2.0-S0021999124009562-main.pdf; Immordino, Gabriele, Vaiuso, Andrea, Da Ronch, Andrea and Righi, Marcello (2025) Predicting transonic flowfields in non–homogeneous unstructured grids using autoencoder graph convolutional networks. Journal of Computational Physics, 524, [113708]. (doi:10.1016/j.jcp.2024.113708 ).

Dostupnost: https://eprints.soton.ac.uk/504234/

Graph autoencoder with mirror temporal convolutional networks for traffic anomaly detection

Autoři: Zhiyu Ren Xiaojie Li Jing Peng a další

Zdroj: Scientific Reports, Vol 14, Iss 1, Pp 1-14 (2024)

Témata: Medicine, Science

Popis souboru: electronic resource

Relation: https://doaj.org/toc/2045-2322

Přístupová URL adresa: https://doaj.org/article/2e41df57b73e4ace8b3d4da03250a356

A Dynamic Multi-Graph Convolutional Spatial-Temporal Network for Airport Arrival Flow Prediction.

Autoři: Huang, Yunyang Yang, Hongyu Yan, Zhen

Zdroj: Aerospace (MDPI Publishing); May2025, Vol. 12 Issue 5, p395, 19p

Témata: GRAPH neural networks, AIR flow, TRAFFIC flow, DEEP learning, MULTIGRAPH, AIR traffic

Variable Structure Learning-Based Spatio-Temporal Graph Convolutional Networks for Chemical Process Quality Prediction with SHAP-Enhanced Interpretability.

Autoři: Tang, Siyuan Zhu, Zheren Zhou, Yuanqiang a další

Zdroj: Processes; Nov2025, Vol. 13 Issue 11, p3751, 22p

Témata: MACHINE learning, GRAPH neural networks, PREDICTIVE validity

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

Autoři: Wu, Zuojun Liu, Xiaojun Zhang, Xiaoling

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