Suchergebnisse - "Graph Autoencoder"

Exploring the latent space distribution of a graph autoencoder trained on 3D models of modernist architecture

Autoren: Erik Bauscher Thomas Wortmann

Quelle: International Journal of Architectural Computing. 23:742-752

Importance weighted variational graph autoencoder

Autoren: Yuhao Tao Lin Guo Shuchang Zhao et al.

Quelle: Complex & Intelligent Systems, Vol 12, Iss 1, Pp 1-16 (2025)

Schlagwörter: Graph neural networks, Variational graph autoencoder, Variational inference, Importance weighted, Electronic computers. Computer science, QA75.5-76.95, Information technology, T58.5-58.64

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/2199-4536; https://doaj.org/toc/2198-6053

Zugangs-URL: https://doaj.org/article/b111fb84353f458ab68cb00bba7ce0ad

Variational Graph Autoencoder for Heterogeneous Information Networks with Missing and Inaccurate Attributes

Autoren: Yige Zhao Jianxiang Yu Yao Cheng et al.

Quelle: Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1. :2067-2078

Schlagwörter: Social and Information Networks (cs.SI), FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Social and Information Networks, Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2311.07929

WAGE: Weight-Sharing Attribute-Missing Graph Autoencoder

Autoren: Wenxuan Tu Sihang Zhou Xinwang Liu et al.

Quelle: IEEE Transactions on Pattern Analysis and Machine Intelligence. 47:5760-5777

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/40184302

GAE4HT: Detecting Hardware Trojans with Graph Autoencoder-Trained on Golden Model Data Flow Graphs

Autoren: Daehyeon Lee Junghee Lee

Quelle: Proceedings of the 20th ACM Asia Conference on Computer and Communications Security. :1175-1187

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

Autoren: Yuhan Guo Runsheng Chen Youssef Boulaksil et al.

Quelle: International Journal of Production Research. :1-27

A graph autoencoder network to measure the geometric similarity of drainage networks in scaling transformation

Autoren: Yu, Huafei Ai, Tinghua Yang, Min et al.

Weitere Verfasser: Yu, Huafei Ai, Tinghua Yang, Min et al.

Quelle: International Journal of Digital Earth. 16(1):1828-1852

Schlagwörter: Natural Sciences, Earth and Related Environmental Sciences, Other Earth Sciences (including Geographical Information Science), Naturvetenskap, Geovetenskap och relaterad miljövetenskap, Annan geovetenskap (Här ingår: Geografisk informationsvetenskap), Physical Geography, Naturgeografi

Zugangs-URL: https://doi.org/10.1080/17538947.2023.2212920

Self-supervised Graph Autoencoder with Node Feature Convolution for Community Detection

Autoren: Haoran Tan Hongkai Xie Xiaofeng Wang et al.

Quelle: Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering ISBN: 9783032002174

A Novel Framework for Predicting Phage-Host Interactions via Host Specificity-Aware Graph Autoencoder

Autoren: Zhen Xiao Han Sun Ankang Wei et al.

Quelle: IEEE Journal of Biomedical and Health Informatics. 29:3069-3078

Schlagwörter: Bacteria, Host-Pathogen Interactions, Computational Biology, Humans, Bacteriophages, Autoencoder, Host Specificity, Algorithms

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/40030240

Enhancing Recommendation Performance via Aspect-Based Review Analysis and Dual Graph Autoencoder

Autoren: Dasom Jeong Haebin Lim Qinglong Li et al.

Quelle: Journal of Intelligence and Information Systems. 31:111-130

DGAEPPO: Graph Autoencoder-Enhanced PPO for Dependency-Aware Edge Tasks

Autoren: Bofeng Zhang Chenlei Xu Simin Wang et al.

Quelle: 2025 IEEE 6th International Conference on Pattern Recognition and Machine Learning (PRML). :134-138

VCGAE++: variational collective graph autoEncoder for multi-behavior recommendation

Autoren: Yingxuan Zhuang Yang Liu Weike Pan et al.

Quelle: Knowledge and Information Systems. 67:8085-8114

Locational False Data Injection Attack Detection in Smart Grid Using Recursive Variational Graph Autoencoder

Autoren: Yufeng Wang Ziyan Lu Jianhua Ma et al.

Quelle: IEEE Internet of Things Journal. 12:13697-13708

Canonical Correlation Analysis aided Dynamic Graph Autoencoder Method for Industrial Process Monitoring

Autoren: Md Mehedi Hassan Zhiwen Chen Syeda Sadia Afrin et al.

Quelle: 2025 IEEE 14th Data Driven Control and Learning Systems (DDCLS). :750-755

A Variational Graph Autoencoder Aided Canonical Correlation Analysis-Based Online Abnormal Patterns Detection Method for Buildings HVAC Systems

Autoren: Xiaogang Pan Qiao Deng Yuanyuan Jiao et al.

Quelle: IEEE Transactions on Consumer Electronics. 71:6867-6879

Hierarchical Vector Quantized Graph Autoencoder with Annealing-Based Code Selection

Autoren: Long Zeng Jianxiang Yu Jiapeng Zhu et al.

Quelle: Proceedings of the ACM on Web Conference 2025. :3772-3782

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2504.12715

Hgae: Heterogeneous Graph Autoencoder-Based Service Bundle Recommendations for Efficient Mashup Development

Autoren: Kaipu Sun Xuanye Wang Meng Xi et al.

Quelle: 2025 IEEE International Conference on Web Services (ICWS). :943-945

Exploring Microbe-drug Association Prediction via Multi-attribute Dual-decoder Graph Autoencoder

Autoren: Wei Liu Xiangcheng Deng Xingen Sun et al.

Quelle: IEEE Journal of Biomedical and Health Informatics. :1-11

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/40168534

Spatiotemporal Graph Autoencoder Network for Skeleton-Based Human Action Recognition

Autoren: Hosam Abduljalil Ahmed Elhayek Abdullah Marish Ali et al.

Quelle: AI, Vol 5, Iss 3, Pp 1695-1708 (2024)

Schlagwörter: human activity analysis, Electronic computers. Computer science, graph autoencoder, deep learning, graph convolutional networks, QA75.5-76.95, skeleton-based human action recognition

Zugangs-URL: https://doaj.org/article/6db5e7cb49744e039fc486429393c144

Prediction of Associations between Nanoparticle, Drug and Cancer Using Variational Graph Autoencoder

Autoren: Emrah İNAN

Quelle: Volume: 26, Issue: 76 167-172
Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi

Schlagwörter: Bilgisayar Görüşü ve Çoklu Ortam Hesaplama (Diğer), Varyasyonel Çizge Otokodlayıcı, Nanoparçacıklar, İlaç-hastalık İlişkisi, Variational Graph Autoencoder, Nanoparticles, Drug–disease Association, Computer Vision and Multimedia Computation (Other), 3. Good health

Dateibeschreibung: application/pdf

Zugangs-URL: https://dergipark.org.tr/tr/pub/deumffmd/issue/82757/1300214