https://github.com/squareRoot3/Rethinking-Anomaly-Detection

"Rethinking Graph Neural Networks for Anomaly Detection" in ICML 2022
https://github.com/squareRoot3/Rethinking-Anomaly-Detection

anomaly-detection deep-learning graph-neural-networks

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"Rethinking Graph Neural Networks for Anomaly Detection" in ICML 2022

• Host: GitHub
• URL: https://github.com/squareRoot3/Rethinking-Anomaly-Detection
• Owner: squareRoot3
• Created: 2022-06-01T02:58:21.000Z (over 2 years ago)
• Default Branch: master
• Last Pushed: 2024-06-25T09:30:38.000Z (6 months ago)
• Last Synced: 2024-08-01T16:33:05.912Z (5 months ago)
• Topics: anomaly-detection, deep-learning, graph-neural-networks
• Language: Python
• Homepage: https://proceedings.mlr.press/v162/tang22b.html
• Size: 17.6 KB
• Stars: 168
• Watchers: 2
• Forks: 26
• Open Issues: 0
• Metadata Files:
  • Readme: readme.md

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README

        
# Rethinking Graph Neural Networks for Anomaly Detection 

This is the official implementation for the following paper:

[Rethinking Graph Neural Networks for Anomaly Detection](https://proceedings.mlr.press/v162/tang22b.html)  

*Jianheng Tang, Jiajin Li, Ziqi Gao, Jia Li*  

ICML 2022

BWGNN has been integrated into [GADBench](https://github.com/squareRoot3/GADBench), a comprehensive benchmark for (semi-)supervised graph anomaly detection.

Dependencies

----------------------

- pytorch 1.9.0

- dgl 0.8.1

- sympy

- argparse

- sklearn

How to run

--------------------------------

The T-Finance and T-Social datasets developed in the paper are on [google drive](https://drive.google.com/drive/folders/1PpNwvZx_YRSCDiHaBUmRIS3x1rZR7fMr?usp=sharing). Download and unzip all files in the `dataset` folder.

`plot.zip` in the above link is used to reproduce Figure 1 and 2 in our paper. You can unzip it and directly run the corresponding `.py` files.

The Yelp and Amazon datasets will be automatically downloaded from the Internet. 

Train BWGNN (homo) on Amazon (40%): 

```

python main.py --dataset amazon --train_ratio 0.4 --hid_dim 64 \

--order 2 --homo 1 --epoch 100 --run 1

```

`amazon` can be replaced by other datasets: `yelp/tfinance/tsocial`

Train BWGNN (hetero) on Yelp (1%):

```

python main.py --dataset yelp --train_ratio 0.01 --hid_dim 64 \

--order 2 --homo 0 --epoch 100 --run 1

```

BWGNN (hetero) only supports Yelp and Amazon.

Train BWGNN (homo) on T-Social (40%):

```

python main.py --dataset tsocial --train_ratio 0.4 --hid_dim 10 \

--order 5 --homo 1 --epoch 100 --run 1

```

If you use this package and find it useful, please cite our ICML paper using the following BibTeX. Thanks! :)

```

@InProceedings{tang2022rethinking,

  title = 	 {Rethinking Graph Neural Networks for Anomaly Detection},

  author =       {Tang, Jianheng and Li, Jiajin and Gao, Ziqi and Li, Jia},

  booktitle = 	 {International Conference on Machine Learning},

  year = 	 {2022},

}

```

You can find a more detailed BibTex or other citation formats [here](https://proceedings.mlr.press/v162/tang22b.html).