ARC

This repository is the official implementation of "ARC: A Generalist Graph Anomaly Detector with In-Context Learning", accepted by NeurIPS 2024.

Setup

conda create -n ARCGAD python=3.8
conda activate ARCGAD
pip install torch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 --index-url https://download.pytorch.org/whl/cu121

pip install --no-index torch-scatter -f https://pytorch-geometric.com/whl/torch-2.1.2+cu121.html
pip install --no-index torch-sparse -f https://pytorch-geometric.com/whl/torch-2.1.2+cu121.html
pip install --no-index torch-cluster -f https://pytorch-geometric.com/whl/torch-2.1.2+cu121.html
pip install --no-index torch-spline-conv -f https://pytorch-geometric.com/whl/torch-2.1.2+cu121.html
pip install torch-geometric==2.3.1

Usage

Due to file size limitations, the tFinance dataset can be downloaded via Google Drive. Just run the script corresponding to the dataset and method you want. For instance:

python main.py --trial 5 --shot 10

Cite

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

@inproceedings{liu2024arc,
  title={ARC: A Generalist Graph Anomaly Detector with In-Context Learning},
  author={Liu, Yixin and Li, Shiyuan and Zheng, Yu and Chen, Qingfeng and Zhang, Chengqi and Pan, Shirui},
  booktitle={Advances in Neural Information Processing Systems},
  year={2024}
}