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Pytorch implementation of paper 'GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks' to appear on WSDM2021

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# GraphSmote
Pytorch implementation of paper ['GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks'](https://arxiv.org/abs/2103.08826) on WSDM2021

## Dependencies
### CPU
- python3
- ipdb
- pytorch1.0
- network 2.4
- scipy
- sklearn

## Dataset
Two processed datasets are published, including BlogCatalog and Cora. For downloading them, please [click](https://drive.google.com/drive/folders/1rfIfRPG7IlzDMAYqQ25HOQmLBCHcECQx?usp=sharing).
Please unzip them in the 'data' folder.

The twitter dataset is obtained from [link](https://github.com/Kagandi/anomalous-vertices-detection/tree/master/data), with [fake id](https://drive.google.com/file/d/1ti1VepCiMQk19mkg0WsYTWznTQfIm3X2/view?usp=sharing). For twitter dataset, node embeddings can be generated with Deepwalk, following this [repo](https://github.com/phanein/deepwalk). Loading the twitter dataset is also provided in data_load.py.

## Configurations

### Architectures
We provide two base architectures, GCN and GraphSage. The default one is GraphSage, and can be set via '--model'.

### Upscale ratios
The default value is 1. If want to make every class balanced instead of using pre-set ratios, please set it to 0 in '--up_scale'.

### Finetuning the decoder
During finetune, set '--setting='newG_cls'' correponds to use pretrained decoder, and set '--setting='recon_newG'' corresponds to also finetune the decoder.

Besides, during finetune, '--opt_new_G' corresponds to update decoder with also classification losses. This option may cause more variance in performance, and usually need more careful hyper-parameter choices.

## GraphSMOTE
Below is an example for the Cora dataset.

### Train
- Pretrain the auto-encoder

<code>python main.py --imbalance --no-cuda --dataset=cora --setting='recon'</code>

Pretrained model can be found in the corresponding checkpoint folder. Rename and set the path to pretrained checkpoint as \[dataset\]\\Pretrained.pth

- Finetune

<code>python main.py --imbalance --no-cuda --dataset=cora --setting='newG_cls' --load=Pretrained.pth</code>

### Baselines
We provide four baselines in this code. They can be configured via the '--setting' arguments. Please refer to the 'get_parser()' function in utils.py.
- Oringinal model: Vanilla backbone models. '--setting='no''
- Over-sampling in raw input domain: Repeat nodes in the minority classes. '--setting='upsampling''
- Reweight: Give samples from minority classes a larger weight when calculating the loss. '--setting='reweight''
- Embed-SMOTE: Perform SMOTE in the intermediate embedding domain. '--setting='embed_up''

Use over-sampling as an example:

<code>python main.py --imbalance --no-cuda --dataset=cora --setting='upsampling'</code>

## Citation

If any problems occur via running this code, please contact us at tkz5084@psu.edu.

Thank you!

GraphSmote

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