Search Results - Graph Convolutional Variational Autoencoder*

Multi-modal graph convolutional network for vessel trajectory prediction based on cooperative intention enhance using conditional variational autoencoder

Authors: Jiang, Junhao Zuo, Yi Li, Zhiyuan

Source: Reliability Engineering and System Safety. 267

Subject Terms: Graph convolutional network, Trajectory prediction, Cooperative intention constructor, Intelligent situational awareness systems, Conditional variational autoencoder, Multi-modal interaction extractor

Access URL: https://research.chalmers.se/publication/549377

Subject Representation Learning from EEG using Graph Convolutional Variational Autoencoders

Authors: Mishra, Aditya Samin, Ahnaf Mozib Etemad, Ali et al.

Source: ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). :1-5

Subject Terms: Signal Processing (eess.SP), FOS: Computer and information sciences, Computer Science - Machine Learning, FOS: Electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Signal Processing, Machine Learning (cs.LG)

Access URL: http://arxiv.org/abs/2501.16626

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

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

Source: Machines, Vol 12, Iss 12, p 894 (2024)

Subject Terms: solar array, photovoltaic array, fault detection, fault diagnosis, graph convolutional network, variational autoencoder, Mechanical engineering and machinery, TJ1-1570

File Description: electronic resource

Relation: https://www.mdpi.com/2075-1702/12/12/894; https://doaj.org/toc/2075-1702

Access URL: https://doaj.org/article/a68e9bb8f0bd42d6b8c9842c6321237e

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

Authors: Liang, Guojun, Postdoktor Tiwari, Prayag Nowaczyk, Sławomir et al.

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

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

File Description: print

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

Anomaly Detection Based on Graph Convolutional Network–Variational Autoencoder Model Using Time-Series Vibration and Current Data

Authors: Seung-Hwan Choi Dawn An Inho Lee et al.

Source: Mathematics, Vol 12, Iss 23, p 3750 (2024)

Subject Terms: anomaly detection, graph convolutional network (GCN), variational autoencoder (VAE), statistical feature, probability-based anomaly score, Mathematics, QA1-939

File Description: electronic resource

Relation: https://www.mdpi.com/2227-7390/12/23/3750; https://doaj.org/toc/2227-7390

Access URL: https://doaj.org/article/c1d52f19b28848b1becfec82e190f2fb

A Guassian Mixture Variational Graph Autoencoder for Node Classification

Authors: Mohadeseh Ghayekhlou Ahmad Nikabadi

Source: AUT Journal of Modeling and Simulation, Vol 56, Iss 1, Pp 103-116 (2024)

Subject Terms: graph convolutional networks (gcns), variational graph autoencoders (vgaes), gaussian mixture model (gmm), Engineering (General). Civil engineering (General), TA1-2040, Electronic computers. Computer science, QA75.5-76.95

File Description: electronic resource

Relation: https://miscj.aut.ac.ir/article_5523_0524f123e5f48ab1ce264793e2624b5a.pdf; https://doaj.org/toc/2588-2953; https://doaj.org/toc/2588-2961

Access URL: https://doaj.org/article/e693f22aa6c74c72968952bf1df4181f

Scalable Graph Convolutional Variational Autoencoders

Authors: Unyi, Dániel Gyires-Tóth, Bálint

Source: 2021 IEEE 15th International Symposium on Applied Computational Intelligence and Informatics (SACI). :467-472

Subject Terms: QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány, 05 social sciences, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 0503 education

File Description: text

Access URL: https://dblp.uni-trier.de/db/conf/saci/saci2021.html#UnyiG21
https://ieeexplore.ieee.org/document/9465579/

Improving Variational Graph Autoencoders With Multi-Order Graph Convolutions

Authors: Lining Yuan Ping Jiang Zhu Wen et al.

Source: IEEE Access, Vol 12, Pp 46919-46929 (2024)

Subject Terms: Variational graph autoencoders, graph convolutional networks, multi-order neighborhood, high-order information, graph representation learning, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

File Description: electronic resource

Relation: https://ieeexplore.ieee.org/document/10477408/; https://doaj.org/toc/2169-3536

Access URL: https://doaj.org/article/025d2e8d9cb746fd8a85da1c8047095c

Prediction of microbe–drug associations based on a modified graph attention variational autoencoder and random forest

Authors: Bo Wang Fangjian Ma Xiaoxin Du et al.

Source: Frontiers in Microbiology, Vol 15 (2024)

Subject Terms: microbe–drug association prediction, variational autoencoder, modified graph convolutional neural network, graph attention network, random forest classifier, computational prediction model, Microbiology, QR1-502

File Description: electronic resource

Relation: https://www.frontiersin.org/articles/10.3389/fmicb.2024.1394302/full; https://doaj.org/toc/1664-302X

Access URL: https://doaj.org/article/5e963e31a3a540a0a7d0d21975537bff

Synergistic Integration of Image-Text Modalities in Telemedicine: VAE-Infused Graph Convolutional Networks for Diagnostic Decision-Making

Authors: Yirui Kuang Hui Jin Jiayu Yang et al.

Source: IEEE Access, Vol 13, Pp 63093-63107 (2025)

Subject Terms: Telemedicine, multimodal fusion, graph neural networks, variational autoencoders, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

File Description: electronic resource

Relation: https://ieeexplore.ieee.org/document/10955270/; https://doaj.org/toc/2169-3536

Access URL: https://doaj.org/article/9c3b4c47a53c4b2a87210659c816f623

Improving Variational Autoencoders for New Physics Detection at the LHC With Normalizing Flows

Authors: Jawahar, Pratik Aarrestad, Thea Chernyavskaya, Nadezda et al.

Subject Terms: Data Management and Data Science, Information and Computing Sciences, Information Systems, anomaly detection, variational auto encoder, normalizing flow, Large Hadron Collider, new physics beyond standard model, graph convolutional network, convolutional neural net, Data management and data science, Information systems

File Description: application/pdf

Access URL: https://escholarship.org/uc/item/9d87s2q3
https://escholarship.org/content/qt9d87s2q3/qt9d87s2q3.pdf

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

Authors: Zhenguo Xu Jun Li Irene Moulitsas et al.

Source: Big Data and Cognitive Computing, Vol 9, Iss 4, p 101 (2025)

Subject Terms: key node identification, community detection, link prediction, small-world network, graph attention networks, variational graph autoencoder, Technology

File Description: electronic resource

Relation: https://www.mdpi.com/2504-2289/9/4/101; https://doaj.org/toc/2504-2289

Access URL: https://doaj.org/article/e8e61df9dc424a059558d2f5b7f862d0

Enhancing Gas Turbine Fault Diagnosis Using a Multi-Scale Dilated Graph Variational Autoencoder Model

Authors: Zhang Kun Li Hongren Wang Xin et al.

Source: IEEE Access, Vol 12, Pp 104818-104832 (2024)

Subject Terms: Gas turbine, fault diagnosis, MG-VAE, noise robustness, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

File Description: electronic resource

Relation: https://ieeexplore.ieee.org/document/10613765/; https://doaj.org/toc/2169-3536

Access URL: https://doaj.org/article/c986c7c9e0e34b09bbf948eb168f8205

GNODEVAE: a graph-based ODE-VAE enhances clustering for single-cell data

Authors: Zeyu Fu Chunlin Chen Song Wang et al.

Source: BMC Genomics, Vol 26, Iss 1, Pp 1-29 (2025)

Subject Terms: Graph attention networks, Neural ordinary differential equation, Variational autoencoders, Clustering, ScRNA-seq, ScATAC-seq, Biotechnology, TP248.13-248.65, Genetics, QH426-470

File Description: electronic resource

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

Access URL: https://doaj.org/article/e02e430dd2cc4cf5a385ba4b16829290

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

File Description: electronic resource

Relation: https://ieeexplore.ieee.org/document/10723293/; https://doaj.org/toc/2169-3536

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

Hierarchical Graph-Convolutional Variational AutoEncoding for Generative Modelling of Human Motion

Authors: Bourached, Anthony Gray, Robert Guan, Xiaodong et al.

Index Terms: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Mathematics - Probability, text

URL: http://arxiv.org/abs/2111.12602

scVGAE: A Novel Approach using ZINB-Based Variational Graph Autoencoder for Single-Cell RNA-Seq Imputation

Authors: Inoue, Yoshitaka

Index Terms: Quantitative Biology - Genomics, Computer Science - Computational Engineering, Finance, and Science, text

URL: http://arxiv.org/abs/2403.08959

GVDTI: graph convolutional and variational autoencoders with attribute-level attention for drug–protein interaction prediction.

Authors: Xuan, Ping Fan, Mengsi Cui, Hui et al.

Source: Briefings in Bioinformatics. Jan2022, Vol. 23 Issue 1, p1-12. 12p.

Subject Terms: PROTEIN-protein interactions, CONVOLUTIONAL neural networks, PROTEIN drugs

VGE: Gene-Disease Association by Variational Graph Embedding

Authors: Peng Han Xiangliang Zhang

Source: International Journal of Crowd Science, Vol 8, Iss 2, Pp 95-99 (2024)

Subject Terms: disease-gene association, graph convolutional network (gcn), variational antoencoder (vae), Technology, Engineering (General). Civil engineering (General), TA1-2040

File Description: electronic resource

Relation: https://www.sciopen.com/article/10.26599/IJCS.2024.9100004; https://doaj.org/toc/2398-7294

Access URL: https://doaj.org/article/433f523c1b194c51bb094eaeb00d9154

Graph variational autoencoder with affinity propagation for community-aware anomaly detection in attributed networks.

Authors: Cao, Zhijie Yang, Chengkun Fan, Xiaoqing et al.

Source: Applied Soft Computing. Jan2026:Part D, Vol. 186, pN.PAG-N.PAG. 1p.

Subject Terms: Anomaly detection (Computer security), Clustering algorithms, Representations of graphs, Computer networks, Principal components analysis