A Flexible Generative Framework for Graph-based Semi-supervised Learning (NeurIPS 2019)

Last update: Oct 11, 2022

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Overview

G3NN

This repo provides a pytorch implementation for the 4 instantiations of the flexible generative framework as described in the following paper:

A Flexible Generative Framework for Graph-based Semi-supervised Learning

Jiaqi Ma*, Weijing Tang*, Ji Zhu, and Qiaozhu Mei. NeurIPS 2019.

(*: equal contribution)

Requirements

See environment.yml. Run conda torch_env create -f environment.yml to install the required packages.

Run the code

Example: python main.py --model lsm_gcn --dataset cora

Cite

@inproceedings{ma2019flexible,
  title={A Flexible Generative Framework for Graph-based Semi-supervised Learning},
  author={Ma, Jiaqi and Tang, Weijing and Zhu, Ji and Mei, Qiaozhu},
  booktitle={Advances in Neural Information Processing Systems},
  pages={3276--3285},
  year={2019}
}

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Comments

Resolve package not found

您好，我用readme中的 conda torch_env create -f environment.yml不能安装，提示没有这个这个命令，只能用conda env create -f environment.yml，然后提示Resolve package not found，不知道z恩么解决呢？

opened by JialongWang1224 1
code error

Hi, I have ran the environment.yml file to create the env. However, I find this error ModuleNotFoundError: No module named 'torch_scatter.scatter_cuda'. I wonder if you have the solution of this error?

opened by Anooyman 1

Owner

Jiaqi Ma

GitHub https://arxiv.org/abs/1905.10769

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2 Dec 17, 2021

A Flexible Generative Framework for Graph-based Semi-supervised Learning (NeurIPS 2019)

Related tags

Overview

G3NN

Requirements

Run the code

Cite

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Comments

Resolve package not found

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Owner

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A Flexible Generative Framework for Graph-based Semi-supervised Learning (NeurIPS 2019)

Related tags

Overview

G3NN

Requirements

Run the code

Cite

You might also like...

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CoSMA: Convolutional Semi-Regular Mesh Autoencoder. From Paper "Mesh Convolutional Autoencoder for Semi-Regular Meshes of Different Sizes"

code for paper "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning" by Zhongzheng Ren*, Raymond A. Yeh*, Alexander G. Schwing.

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Source codes for the paper "Local Additivity Based Data Augmentation for Semi-supervised NER"

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Comments

Resolve package not found

code error

Owner

Jiaqi Ma

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code for paper "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning" by Zhongzheng Ren, Raymond A. Yeh, Alexander G. Schwing.