A PyTorch implementation of "ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning", CIKM-21
- python==3.6.1
- dgl==0.4.1
- matplotlib==3.3.4
- networkx==2.5
- numpy==1.19.2
- pyparsing==2.4.7
- scikit-learn==0.24.1
- scipy==1.5.2
- sklearn==0.24.1
- torch==1.8.1
To install all dependencies:
pip install -r requirements.txt
Anomalies have been injected into three datasets under the ./dataset directory.
Please refer to GRAND-Lab/CoLA for graph anomaly injection.
To train and evaluate on Cora:
python run.py --expid 1 --device cuda:0 --runs 1 --alpha 0.8
To train and evaluate on Citeseer:
python run.py --dataset citeseer --expid 2 --device cuda:0 --runs 1 --alpha 0.6
To train and evaluate on Pubmed:
python run.py --dataset pubmed --expid 3 --device cuda:0 --runs 1 --alpha 0.8 --negsamp_ratio_patch 6 --negsamp_ratio_context 1
If you use our code in your research, please cite the following article:
@inproceedings{jin2021anomaly,
title={ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning},
author={Ming Jin and Yixin Liu and Yu Zheng and Lianhua Chi and Yuan-Fang Li and Shirui Pan},
booktitle={Proceedings of the 30th ACM International Conference on Information \& Knowledge Management},
year={2021}
}