Fanghui Bi

According to our database, Fanghui Bi authored at least 10 papers between 2021 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Graph Linear Convolution Pooling for Learning in Incomplete High-Dimensional Data.

Fanghui Bi, Tiantian He, Yew-Soon Ong, Xin Luo

IEEE Trans. Knowl. Data Eng., April, 2025

2024

A Fast Nonnegative Autoencoder-Based Approach to Latent Feature Analysis on High-Dimensional and Incomplete Data.

Fanghui Bi, Tiantian He, Xin Luo

IEEE Trans. Serv. Comput., 2024

A Novel Spatiotemporal Coupling Graph Convolutional Network.

Fanghui Bi

CoRR, 2024

Graph Clustering with Cross-View Feature Propagation.

Zhixuan Duan, Zuo Wang, Fanghui Bi

CoRR, 2024

Discrete Multi-View Feature Propagation Preserving Graph Clustering.

Zhixuan Duan, Zuo Wang, Fanghui Bi, Tiantian He

Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, 2024

SCG: A Novel Spatiotemporal Coupling Graph Convolutional Network-Incorporated Approach for Dynamic QoS Estimation.

Fanghui Bi, Tiantian He

Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, 2024

2023

Proximal Alternating-Direction-Method-of-Multipliers-Incorporated Nonnegative Latent Factor Analysis.

Fanghui Bi, Xin Luo, Bo Shen, Hongli Dong, Zidong Wang

IEEE CAA J. Autom. Sinica, June, 2023

Two-Stream Graph Convolutional Network-Incorporated Latent Feature Analysis.

Fanghui Bi, Tiantian He, Yuetong Xie, Xin Luo

IEEE Trans. Serv. Comput., 2023

2022

A Two-Stream Light Graph Convolution Network-based Latent Factor Model for Accurate Cloud Service QoS Estimation.

Fanghui Bi, Tiantian He, Xin Luo

Proceedings of the IEEE International Conference on Data Mining, 2022

2021

A Proximal Alternating-direction-method-of-multipliers-based Nonnegative Latent Factor Model.

Fanghui Bi, Di Wu

Proceedings of the 2021 IEEE International Conference on Big Knowledge, 2021