Rethinking Graph Neural Networks for Anomaly Detection

This is a PyTorch implementation of

Rethinking Graph Neural Networks for Anomaly Detection

Dependencies

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