ConGNN

These are the code and data for the paper: Controlled graph neural networks with denoising diffusion for anomaly detection, Expert Systems with Applications, 2024. https://www.sciencedirect.com/science/article/pii/S0957417423020353

Citation

@article{li2024controlled,
    title={Controlled graph neural networks with denoising diffusion for anomaly detection},
    author={Li, Xuan and Xiao, Chunjing and Feng, Ziliang and Pang, Shikang and Tai, Wenxin and Zhou, Fan},
    journal={Expert Systems with Applications},
    volume={237},
    pages={121533},
    year={2024},
    publisher={Elsevier}
}

Requirements

python==3.7.3 pytorch>=1.4 dgl-cuda11.6 sklearn>=0.20.1 numpy>=1.16 networkx>=2.1

How to train

$For\ the\ cora\ dataset$

Run the main file

python main.py --dataset cora --module GAE --nu 0.1 --lr 0.001 --batch-size 64  --n-hidden 64 --n-layers 2 --weight-decay 0.0005 --n-epochs 1000 --early-stop

Run the generate file

python ddpm/feature_train.py --config ddpm/config/cora_train.json

python ddpm/feature_test.py --config ddpm/config/cora_test.json

Run the main file

python main.py --dataset cora --module GraphSAGE --nu 0.1 --lr 0.001 --batch-size 128 --n-hidden 64 --n-layers 2 --weight-decay 0.0005 --n-epochs 1000 --early-stop

$For\ the\ citeseer\ dataset$

Run the main file

python main.py --dataset citeseer --module GAE --nu 0.1 --lr 0.001 --batch-size 64 --n-hidden 64 --n-layers 2 --weight-decay 0.0005 --n-epochs 1000 --early-stop

Run the generate file

python ddpm/feature_train.py --config ddpm/config/citeseer_train.json

python ddpm/feature_test.py --config ddpm/config/citeseer_test.json

Run the main file

python main.py --dataset citeseer --module GraphSAGE --nu 0.1 --lr 0.001 --batch-size 128 --n-hidden 64 --n-layers 2 --weight-decay 0.0005 --n-epochs 1000 --early-stop

$For\ the\ pubmed\ dataset$

Run the main file

python main.py --dataset pubmed --module GAE --nu 0.1 --lr 0.001 --batch-size 64 --n-hidden 64 --n-layers 2 --weight-decay 0.0005 --n-epochs 1000 --early-stop

Run the generate file

python ddpm/feature_train.py --config ddpm/config/pubmed_train.json

python ddpm/feature_test.py --config ddpm/config/pubmed_test.json

Run the main file

python main.py --dataset pubmed --module GCN --nu 0.1 --lr 0.001 --batch-size 128 --n-hidden 64 --n-layers 2 --weight-decay 0.0005 --n-epochs 200 --early-stop

Folder descriptions

dateset: This is used to process datasets and divide training, verification and test datasets.

networks: This is used to store the graph neural network model used in training.

train: This is used for training and loss calculation.

utils: This is some tool classes.

ddpm: Relevant documents of DDPM are stored here