Node Dependent Local Smoothing for Scalable Graph Learning

Last update: Nov 28, 2022

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Overview

Node Dependent Local Smoothing for Scalable Graph Learning

Requirements

Environments: Xeon Gold 5120 (CPU), 384GB(RAM), TITAN RTX (GPU), Ubuntu 16.04 (OS)

The PyTorch version we use is torch 1.7.1+cu110. Please refer to the official website -- https://pytorch.org/get-started/locally/ -- for the detailed installation instructions.

To install other requirements:

pip install -r requirements.txt

Training

To train the model(s) in the paper, run this command:

cd src; python train.py --dataset cora/citeseer/pubmed

Please refer to the Appendix for the detailed hyperparameters.

Node Classification Results

Transductive Setting:
Inductive Setting:
Efficiency Comparison:

Owner

Wentao Zhang

Make each day worth!

GitHub Repository

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Node Dependent Local Smoothing for Scalable Graph Learning

Related tags

Overview

Node Dependent Local Smoothing for Scalable Graph Learning

Requirements

Training

Node Classification Results

Owner

Wentao Zhang

Implementation of SSMF: Shifting Seasonal Matrix Factorization

An Implementation of Fully Convolutional Networks in Tensorflow.

Revitalizing CNN Attention via Transformers in Self-Supervised Visual Representation Learning

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Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.

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