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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
Last synced: 10 days ago
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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 (5 months ago)
- Last Synced: 2024-08-01T16:33:05.912Z (3 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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- StarryDivineSky - squareRoot3/Rethinking-Anomaly-Detection
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).