This is the supplementary file and PyTorch implementation 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.

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

We implement all the experiments in Python 3.7, except that the compressed sparse matrix parallel program is written with CUDA C and compiled with CUDA 11.1. All empirical tests are uniformly deployed on a server with a 2.4-GHz Intel Xeon 4214R CPU, four NVIDIA RTX 3090 GPUs, and 128-GB RAM.

pip install -r requirements.txt

Two real QoS data collected by the WS-Dream system are applied in our experiments, which are the largest publicly-available QoS datasets and widely adopted in prior studies.

Please tune the hyper parameters in run.py and run it.

Please see more information in the manuscript.