Oak-B / TSC-2022-TGLFA

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

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TSC-2022-TGLFA

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

Enviroment Requirement

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

Dataset

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.

Run

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

Others

Please see more information in the manuscript.

About

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


Languages

Language:Python 75.4%Language:Cuda 18.4%Language:C++ 6.2%