Výsledky vyhľadávania - "Graph autoencoder (GAE)"

Graph autoencoder (GAE) for community detection in social networks

Autori: Pratibha Joshi Buddha Singh

Zdroj: International Journal of Data Science and Analytics. 20:3693-3705

Correction to: Graph autoencoder (GAE) for community detection in social networks

Autori: Pratibha Joshi Buddha Singh

Zdroj: International Journal of Data Science and Analytics. 20:5033-5035

Graph autoencoder (GAE) for community detection in social networks.

Autori: Joshi, Pratibha Singh, Buddha

Zdroj: International Journal of Data Science & Analytics; Oct2025, Vol. 20 Issue 4, p3693-3705, 13p

Correction to: Graph autoencoder (GAE) for community detection in social networks.

Autori: Joshi, Pratibha Singh, Buddha

Zdroj: International Journal of Data Science & Analytics; Oct2025, Vol. 20 Issue 5, p5033-5035, 3p

The effectiveness of intervention measures on MERS-CoV transmission by using the contact networks reconstructed from link prediction data

Autori: Eunmi Kim Yunhwan Kim Hyeonseong Jin a ďalší

Zdroj: Front Public Health
Frontiers in Public Health, Vol 12 (2024)

Predmety: 0301 basic medicine, graph autoencoder (GAE), Disease Outbreaks, 3. Good health, MERS-CoV, network-based models, 03 medical and health sciences, 0302 clinical medicine, Republic of Korea, Quarantine, Middle East Respiratory Syndrome Coronavirus, Humans, Computer Simulation, Public Health, Public aspects of medicine, RA1-1270, Contact Tracing, Coronavirus Infections, interventions, link prediction

Prístupová URL adresa: https://pubmed.ncbi.nlm.nih.gov/38827618
https://doaj.org/article/8bc986ee7ba6436ebd135f26648c5590

Modelling and solving sustainable supply chain network design based on graph autoencoder clustering algorithm.

Autori: Guo, Yuhan Chen, Runsheng Boulaksil, Youssef a ďalší

Zdroj: International Journal of Production Research; Dec2025, Vol. 63 Issue 24, p10000-10026, 27p

GAM-MDR: probing miRNA–drug resistance using a graph autoencoder based on random path masking.

Autori: Zhou, Zhecheng Du, Zhenya Jiang, Xin a ďalší

Zdroj: Briefings in Functional Genomics. Jul2024, Vol. 23 Issue 4, p475-483. 9p.

Predmety: *DEEP learning, *GENE expression, *MICRORNA, *ACQUISITION of data, *DISEASE progression, *THERAPEUTICS

Unsupervised clustering based on a graph attention network reveals high-risk subgroups among ICU dementia patients for enhanced mortality prediction.

Autori: Li, Chuanxiong Zhang, Lei Zhang, Xiaoyu a ďalší

Zdroj: European Journal of Medical Research; 11/10/2025, Vol. 30 Issue 1, p1-18, 18p

Defense Strategy Against False Data Injection Attacks on Cyber–Physical System for Vehicle–Grid Based on KNN-GAE.

Autori: Li, Qiuyan Song, Dawei Wang, Yuanyuan a ďalší

Zdroj: Energies (19961073); Oct2025, Vol. 18 Issue 19, p5215, 24p

Predmety: CYBER physical systems, K-nearest neighbor classification, GRAPH neural networks, COMPUTER network protocols, ROBUST control, DATA integrity

An unsupervised intelligent warning model for drilling kick risk based on multi-temporal feature coupling.

Autori: De-Tao Zhou Zhao-Peng Zhu Tao Pan a ďalší

Zdroj: Petroleum Science (KeAi Communications Co.); Sep2025, Vol. 22 Issue 9, p3613-3626, 14p

Predmety: GRAPH neural networks, FALSE alarms, FEATURE extraction, TIME series analysis

Hyperspectral Band Selection With Iterative Graph Autoencoder

Autori: Zhou, Yuan Yao, Qingren Huo, Shuwei a ďalší

Predmety: Feature extraction, Iterative methods, Symmetric matrices, Hyperspectral imaging, Convolution, Correlation, Task analysis, Graph autoencoder (GAE), graph representation, hyperspectral band selection (BS), representativeness, Geochemistry & Geophysics, Engineering, Remote Sensing, Imaging Science & Photographic Technology, Electrical & Electronic, IMAGE

Relation: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING; http://ir.qdio.ac.cn/handle/337002/182316

Dostupnosť: http://ir.qdio.ac.cn/handle/337002/182316
https://doi.org/10.1109/TGRS.2023.3273776

CyberVeriGNN: A Graph Neural Network‐Based Approach for Detecting Fake Cyber Threat Intelligence.

Autori: Huang, Congyu Wang, Chenxiao

Zdroj: Security & Privacy. Sep/Oct2025, Vol. 8 Issue 5, p1-15. 15p.

Predmety: *CYBER intelligence (Computer security), *GRAPH neural networks, *INTERNET security, *DEEP learning, *DECEPTION, *OUTLIER detection, *STATISTICAL reliability

scLEGA: an attention-based deep clustering method with a tendency for low expression of genes on single-cell RNA-seq data.

Autori: Liu, Zhenze Liang, Yingjian Wang, Guohua a ďalší

Zdroj: Briefings in Bioinformatics; Sep2024, Vol. 25 Issue 5, p1-13, 13p

Predmety: RNA sequencing, SCALABILITY, GENES, HETEROGENEITY, TISSUES

Structured Deep Graph Clustering Network Based on Consistency Constraint.

Autori: Huang, Bin Wang, Denghui

Zdroj: International Journal of Pattern Recognition & Artificial Intelligence; Aug2024, Vol. 38 Issue 10, p1-21, 21p

Predmety: FEATURE extraction, TASK analysis, DATA analysis, CLASSIFICATION

Customer Segmentation and Need Analysis Based on Sentiment Network of Online Reviewers and Graph Embedding.

Autori: Mengyuan Shen Bohan Feng Aoxiang Cheng a ďalší

Zdroj: Journal of Mechanical Design. Apr2025, Vol. 147 Issue 4, p1-17. 17p.

Predmety: *CUSTOMER satisfaction, *SOCIAL network analysis, *PRODUCT attributes, *SENTIMENT analysis, *AUTOENCODERS, *BIPARTITE graphs

A deep learning approach for polyline and building simplification based on graph autoencoder with flexible constraints.

Autori: Yan, Xiongfeng Yang, Min

Zdroj: Cartography & Geographic Information Science; Jan2024, Vol. 51 Issue 1, p79-96, 18p

Predmety: DEEP learning, FOURIER transforms, CARTOGRAPHY, TRIANGULATION

Node embedding-based graph autoencoder outlier detection for adverse pregnancy outcomes.

Autori: Khan, Wasif Zaki, Nazar Ahmad, Amir a ďalší

Zdroj: Scientific Reports; 11/14/2023, Vol. 13 Issue 1, p1-13, 13p

Predmety: OUTLIER detection, PREGNANCY outcomes, LOW birth weight, KNOWLEDGE graphs, PREMATURE labor, MACHINE learning

Attention-based deep clustering method for scRNA-seq cell type identification.

Autori: Li, Shenghao Guo, Hui Zhang, Simai a ďalší

Zdroj: PLoS Computational Biology; 11/10/2023, Vol. 19 Issue 11, p1-19, 19p

Predmety: RNA sequencing, CLUSTER analysis (Statistics), DEEP learning, BREAST cancer, DATA analysis, HETEROGENEITY, IDENTIFICATION

ConSpaS: a contrastive learning framework for identifying spatial domains by integrating local and global similarities.

Autori: Wu, Siyao Qiu, Yushan Cheng, Xiaoqing

Zdroj: Briefings in Bioinformatics; Nov2023, Vol. 24 Issue 6, p1-12, 12p

Predmety: GENE expression, IMAGE representation

Interpretable Failure Localization for Microservice Systems Based on Graph Autoencoder.

Autori: Sun, Yongqian Lin, Zihan Shi, Binpeng a ďalší

Zdroj: ACM Transactions on Software Engineering & Methodology; Feb2025, Vol. 34 Issue 2, p1-28, 28p

Predmety: DATA augmentation, AUTOENCODERS, DEEP learning, FAILURE (Psychology), DATA logging, SUPERVISED learning