This is the source code of TNNLS paper Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning (CoLA).

This code requires the following:

• Python>=3.7
• PyTorch>=1.8.1
• Numpy>=1.19.2
• Scipy>=1.6.2
• Scikit-learn>=0.24.1
• Networkx>=2.5.1
• Tqdm>=4.59.0
• DGL==0.4.0 (Do not use the version which is newer than that!)

This is a pre-processing step which injects anomalies into the original clean datasets. Take Cora dataset as an example:

python inject_anomaly.py --dataset cora

After anomaly injection, the disturbed datasets are saved into "dataset" folder.

Note that the disturbed ACM dataset is directly borrowed from the code of DOMINANT and do not need injection.

This step is to run the CoLA framework to detect anomalies in the network datasets. Take Cora dataset as an example:

python run.py --dataset cora

The hyperparameters are set to be the values reported in our paper. You can change the default values of other parameters to simulate different conditions.

If you compare with, build on, or use aspects of CoLA framework, please cite the following:

@article{liu2021anomaly,
  title={Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning},
  author={Liu, Yixin and Li, Zhao and Pan, Shirui and Gong, Chen and Zhou, Chuan and Karypis, George},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  year={2021},
  publisher={IEEE}
}