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Spatiotemporal Graph Neural Network-Incorporated Latent Factorization of Tensors for Dynamic QoS Estimation

Xin Luo Fanghui Bi Tiantian He

自动化学报（英文版）2026，Vol.13Issue(7)：1642-1656,15.

自动化学报（英文版）2026，Vol.13Issue(7)：1642-1656,15.DOI:10.1109/JAS.2025.125750

Spatiotemporal Graph Neural Network-Incorporated Latent Factorization of Tensors for Dynamic QoS Estimation

Xin Luo ¹Fanghui Bi ²Tiantian He³

作者信息

1. College of Computer and Information Science,Southwest University,Chongqing 400715,China
2. College of Computer and Information Science,Southwest University,Chongqing 400715,China||NetEase,Hangzhou 310000,China
3. Center for Frontier AI Research,Institute of High Performance Computing,Singapore Institute of Manufacturing Technology(SIMTech),Agency for Science,Technology and Research(A*STAR),Singapore 699010,Singapore
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摘要

关键词

Cloud service/data science/dynamic quality-of-ser-vice estimation/graph convolutional networks(GCNs)/latent factor-ization of tensors/latent feature analysis/non-euclidean data/repre-sentation learning/tensor product

Key words

引用本文复制引用

Xin Luo,Fanghui Bi,Tiantian He..Spatiotemporal Graph Neural Network-Incorporated Latent Factorization of Tensors for Dynamic QoS Estimation[J].自动化学报（英文版）,2026,13(7):1642-1656,15.

基金项目

This work was supported by the National Key Research and Development Program of China(2024YFF0908200),the National Natural Science Foundation of China(62272078),and Chongqing Natural Science Foundation(CSTB2023NSCQ-LZX0069). （2024YFF0908200）

自动化学报（英文版）

ISSN：2329-9266

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