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

Last update: Dec 14, 2022

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}
}

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

Related tags

Overview

ANEMONE

Dependencies

Usage

Citation

Owner

Graph Analysis & Deep Learning Laboratory, GRAND

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

Related tags

Overview

ANEMONE

Dependencies

Usage

Citation

Owner

Graph Analysis & Deep Learning Laboratory, GRAND

Optimizing synthesizer parameters using gradient approximation

Contrastive Multi-View Representation Learning on Graphs

[CVPR 2016] Unsupervised Feature Learning by Image Inpainting using GANs

Teaches a student network from the knowledge obtained via training of a larger teacher network

Research code for Arxiv paper "Camera Motion Agnostic 3D Human Pose Estimation"

Process JSON files for neural recording sessions using Medtronic's BrainSense Percept PC neurostimulator

Cycle Consistent Adversarial Domain Adaptation (CyCADA)

Code for ACM MM2021 paper "Complementary Trilateral Decoder for Fast and Accurate Salient Object Detection"

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