Self-supervised learning on Graph Representation Learning (node-level task)

Last update: Dec 31, 2021

Related tags

Deep Learning graph_SSL

Overview

graph_SSL

Self-supervised learning on Graph Representation Learning (node-level task)

How to run the code

To run GRACE, sh run_GRACE.sh
To run GCA, sh run_GCA.sh
To run BGRL, sh run_BGRL.sh

Experimental Results

You can check detailed experimental results in results folder.

name	WikiCS	Amazon Computers	Amazon Photo	Coauthor CS	Coauthor Physics
`GRACE`	78.41	86.44	90.02	92.63	OOM
`GCA`	78.42	88.06	89.85	92.77	OOM
`BGRL`	79.50	88.338	92.83	92.49	94.89

Implemented

Implemented paper list

name	Paper
`GRACE`	paper
`GCA`	paper
`BGRL`	paper

Codes borrowed from

name	Implementation Code	Paper
`GRACE`	Implementation	paper
`GCA`	Implementation	paper
`Bootstrap Your Own Latent`	Implementation	paper
`SelfGNN`	Implementation	paper

Owner

Namkyeong Lee

GitHub Repository

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Self-supervised learning on Graph Representation Learning (node-level task)

Related tags

Overview

graph_SSL

How to run the code

Experimental Results

Implemented

Codes borrowed from

Owner

Namkyeong Lee

[ECCV'20] Convolutional Occupancy Networks

Preprossing-loan-data-with-NumPy - In this project, I have cleaned and pre-processed the loan data that belongs to an affiliate bank based in the United States.

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