Rethinking Graph Neural Networks for Anomaly Detection
This is a PyTorch implementation of
Rethinking Graph Neural Networks for 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. Download and unzip it into dataset.
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