A PyTorch implementation of "ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning", CIKM-21

Related tags

Deep Learninganomaly-detectioncontrastive-learning
Overview

ANEMONE

A PyTorch implementation of "ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning", CIKM-21

Dependencies

  • 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

Usage

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

Citation

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}
}
Owner
Graph Analysis & Deep Learning Laboratory, GRAND
GRaph ANalysis & Deep learning Laboratory (GRAND Lab) at Monash University
Graph Analysis & Deep Learning Laboratory, GRAND
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