Many key applications depend on graph data. To tailor this need, we just open-sourced the first comprehensive graph outlier detection library--PyGOD.
PyGOD contains more than 10 latest graph outlier detectors, which are built on [PyTorch](https://pytorch.org) and [PyG](https://www.pyg.org). It features:
- unified and simple API as [PyOD](https://github.com/yzhao062/pyod): using GNNs for outlier detection within 5 lines of code
- full documentation and examples
- for both academic use and industry app, all you need to prepare is the data in PyG format.
PyGOD is a collaborative effort among UIC, CMU, ASU, IIT, and BUAA. We commit to providing long-term maintenance and keep adding new models to the library. It is also our goal to promote graph outlier detection methods to broader audiences. If you encounter a bug or have any suggestions please [fill an issue](https://github.com/pygod-team/pygod/issues) or reach us via email <devpygod.org>. Also, feel free to try it out with your code!
We appreciate every star, fork, and follow.