TopoGDN

TopoGDN

Project Name

Multivariate Time-Series Anomaly Detection based on Enhancing Graph Attention Networks with Topological Analysis

Brief Description

This project applies graph attention networks combined with topological analysis to detect anomalies in multivariate time series. It leverages research in topological graph neural networks and graph neural networks to effectively analyze complex time series data.

Installation Steps

To install this project, you need to install the following Python libraries:

pip install torch==1.13.1+cu117
pip install torch-cluster==1.6.0+pt113cu116
pip install torch-geometric==1.7.1
pip install torch-scatter==2.1.0+pt113cu116
pip install torch-sparse==0.6.15+pt113cu116
pip install torch-spline-conv==1.2.1+pt113cu116
pip install pyg-lib==0.2.0+pt113cu116

Build the torch_persistent_homology Module

After completing the above steps, you need to compile the torch_persistent_homology C++ module into a Python module. This can be done by running the following command in the project root directory:

python setup.py build_ext --inplace

How to Use

Run the main program:

python main.py

Dataset Information

• SWAT and WADI Datasets: These can be obtained from iTrust.
• SMD Dataset: Please refer to https://github.com/17000cyh/IMDiffusion.

Acknowledgement

Thanks to the following works for sharing the code repository:

@InProceedings{Horn22a,
  author = {Horn, Max and {De Brouwer}, Edward and Moor, Michael and Moreau, Yves and Rieck, Bastian and Borgwardt, Karsten},
  title = {Topological Graph Neural Networks},
  year = {2022},
  booktitle = {International Conference on Learning Representations~(ICLR)},
  url = {https://openreview.net/pdf?id=oxxUMeFwEHd},
}
@inproceedings{deng2021graph,
  title = {Graph neural network-based anomaly detection in multivariate time series},
  author = {Deng, Ailin and Hooi, Bryan},
  booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
  volume = {35},
  number = {5},
  pages = {4027--4035},
  year = {2021}
}