Self-Supervised Graph Anomaly Detection via Reconstruction Enhancement and Multiscale Contrastive Learning

This is the source code of Information Sciences paper [Self-Supervised Graph Anomaly Detection via Reconstruction Enhancement and Multiscale Contrastive Learning] (GADREMC).

Requirements

• python==3.8
• dgl==0.4.3
• matplotlib==3.7.5
• networkx==2.8.8
• numpy==1.23.5
• pyparsing==2.4.7
• scikit-learn==1.0.2
• scipy==1.10.1
• sklearn==0.24.1
• torch==2.0.0
• tqdm==4.67.1

To install all requirements:

Usage

To train and evaluate on BlogCatalog:

python run.py --device cuda:0 --expid 1 --dataset BlogCatalog  --auc_test_rounds 256 --alpha 0.6 --beta 0.6  --gamma 0.6

To train and evaluate on Flickr:

python run.py --device cuda:0 --expid 2 --dataset Flickr  --auc_test_rounds 256 --alpha 0.8 --beta 0.6  --gamma 0.0

To train and evaluate on ACM:

python run.py --device cuda:0 --expid 3 --dataset ACM  --auc_test_rounds 256 --alpha 0.6 --beta 0.2  --gamma 0.8

To train and evaluate on Cora:

python run.py --device cuda:0 --expid 4 --dataset cora  --auc_test_rounds 256 --alpha 1.0 --beta 0.6  --gamma 0.0

To train and evaluate on CiteSeer:

python run.py --device cuda:0 --expid 5 --dataset citeseer  --auc_test_rounds 256 --alpha 0.8 --beta 0.6  --gamma 0.2

To train and evaluate on PubMed:

python run.py --device cuda:0 --expid 6 --dataset pubmed  --auc_test_rounds 256 --alpha 0.6 --beta 0.2  --gamma 0.0

Cite

If you use our code in your research, please cite the following article: