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pygod-team / pygod

A Python Library for Graph Outlier Detection (Anomaly Detection)
https://pygod.org
BSD 2-Clause "Simplified" License
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Add tutorials for hyperparamer tuning #19

Closed INF800 closed 1 year ago

INF800 commented 2 years ago

Hi, wide collection of unsupervised algorithms is amazing. But if there aren't sufficient examples on tuning them, other developers may never use it.

I am planning to use these algorithms on publicly available graphs and write tutorials on the same.

I have major experience in deep learning but not in graph neural networks. I can pull this off with sufficient amount of help on underlying algorithms

yzhao062 commented 2 years ago

Hi there, thanks for posting this. One thing to note is unsupervised models, especially anomaly detection algorithms, are notoriously hard to tune. We have some recent works in this direction for unsupervised outlier model selection/tuning [1], while it is still unclear how to do this deep models like GNNs.

You are welcome to propose or bring some new ideas for this. Let us whether we could work something out together. Thanks

[1] Zhao, Y., Rossi, R. and Akoglu, L., 2021. Automatic Unsupervised Outlier Model Selection. Advances in Neural Information Processing Systems, 34.

INF800 commented 2 years ago

Hi, thankyou for the response.

Aa a start I am planning to use it for time series data.

Data: https://github.com/d-ailin/GDN

INF800 commented 2 years ago

Hi, thankyou for the response.

Aa a start I am planning to use it for time series data.

Data: https://github.com/d-ailin/GDN

Let me know if this works

YingtongDou commented 2 years ago

Data: https://github.com/d-ailin/GDN

Our library is designed for handling anomalies in graph data, we would not consider applying it to time-series data for now.