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This is the supplementary file for the paper entitled “A Fast Nonnegative Autoencoder-Based Approach to Latent Feature Analysis on High-Dimensional and Incomplete Data”. Additional tables and figures are put into this file and cited by this paper.

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This is the supplementary file, PyTorch implementation, and prepared datasets for the paper entitled “Graph Linear Convolution Pooling for Learning in Incomplete High-Dimensional Data”.

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This is the PyTorch implementation for the paper entitled "A Two-Stream Light Graph Convolution Network-based Latent Factor Model for Accurate Cloud Service QoS Estimation", which has been acceppted by ICDM2022.

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This is the supplementary file for the paper entitled “Dynamic QoS Estimation via Spatiotemporal Graph Convolutional Network-Incorporated Latent Factorization of Tensors”. The proofs, details of experimental settings, additional tables and figures are put into this file and cited.

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This is the supplementary file and PyTorch implementation for the paper entitled “Two-Stream Graph Convolutional Network-Incorporated Latent Feature Analysis”.

tsc-2022-tglfa icon tsc-2022-tglfa

This is the PyTorch implementation for the paper entitled "Two-Stream Graph Convolutional Network- Incorporated Latent Feature Analysis".

tsc-2022-tglfa-supplementary-file icon tsc-2022-tglfa-supplementary-file

This is the supplementary file for the paper entitled “Two-Stream Graph Convolutional Network-Incorporated Latent Feature Analysis”. Additional tables and figures are put into this file and cited by the paper.

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