Pygod

What's Changed
* Add two detectors: [`GADNR`](https://docs.pygod.org/en/latest/generated/pygod.detector.GADNR.html) by YingtongDou and [`DMGD`](https://docs.pygod.org/en/latest/generated/pygod.detector.DMGD.html) by kayzliu
* Add tutorial for [score conversion](https://docs.pygod.org/en/latest/tutorials/2_convert.html#sphx-glr-tutorials-2-convert-py)
* Add tutorial for [GPU training](https://docs.pygod.org/en/latest/minibatch.html)
* Multiple bugs fixed

Greatly appreciate our community contributors helping improve PyGOD: OldPanda, ParthaPratimBanik, ahmed3amerai

**Full Changelog**: https://github.com/pygod-team/pygod/compare/v1.0.0...v1.1.0

We are thrilled to release PyGOD v1.0.0, a comprehensive open-source graph outlier detection library in the PyG ecosystem.

PyGOD contains more than 10 latest graph outlier detectors, which are built on PyTorch and PyG. It features:
- unified and simple API: detector.fit, detector.predict
- full documentation and examples at [docs.pygod.org](docs.pygod.org)
- all you need to prepare a PyG Data object

New features in recent versions:
- built-in data and benchmark: utils.load_data
- modularized components: nn.encoder, nn.decoder, nn.fuctional, etc.
- extensive utility functions: metric, generator, utils, etc.

If you encounter a bug or have any suggestions please fill an issue or reach us via email at devpygod.org. Also, feel free to try it out with your code! We appreciate every star, fork, and follow.

We are excited to announce the final pre-alpha release, PyGOD v0.4, which marks a major milestone in our development. Following bug fixes and minor improvements, we plan to release v1.0. Your feedback and suggestions are appreciated. ⚠️ Please note that this version is NOT forward compatible and some APIs have changed. Here are the major changes in this release:

Enhanced Base Class
- `Detector`: base class for all detectors.
- `DeepDetector`: base class for all deep learning based detectors.

Simplied APIs
- Removed `predict_proba` and `predict_confidence`.
- Use `predict(return_prob=True, return_conf=True)` instead.

Modularized Detectors
We now introduce multiple modules to improve the code reusability and extendibility.
- `nn`: all base models inherit `torch.nn.Module`
- `nn.encoder`:
- `nn.decoder`:
- `nn.functional`: loss function, etc.
Also, we changed the name of several modules to improve the clarity.
- `models`→`detector`
- `metrics`→`metric`

More Utility Functions
- `to_edge_score`: edge outlier score converter
- `to_graph_score`: graph outlier score converter
- `init_detector`: detector initializer
- `init_nn`: neural network initializer

Updated Requirements
- PyGOD now requires Python 3.8+
- PyTorch 2.0 and PyG 2.3.0 support
- Enabled model compile via `detector(compile_model=True)` (beta)

And Many More
- More comprehensive test coverage (almost 100%)
- Reorganized documentation for better readability
- Merge `MLPAE` and `GCNAE` to `GAE`
- Most of the deep detectors support specifying various backbone from PyG
- Retrieve learned embedding from fitted deep detectors with `save_emb=True` by `detector.emb`

What's Changed
* add edge drop probability to structural outlier injection
* update benchmark script with more datasets.
* multiple minor fixes by cshjin YingtongDou kayzliu

New Contributors
* cshjin made their first contribution in https://github.com/pygod-team/pygod/pull/40

What's New

- We release the first comprehensive node-level graph outlier detection benchmark, and the paper is available on [arXiv](https://arxiv.org/abs/2206.10071). See [benchmark](https://github.com/pygod-team/pygod/tree/main/benchmark) and [data](https://github.com/pygod-team/data) for more details.
- Add new models [SCAN](https://docs.pygod.org/en/latest/pygod.models.html#scan), [Radar](https://docs.pygod.org/en/latest/pygod.models.html#radar), and [ANOMALOUS](https://docs.pygod.org/en/latest/pygod.models.html#anomalous).
- Accelerate GAAN and CONAD by vectorization. Up to 40x faster.
- Add a new metric [eval_ndcg](https://docs.pygod.org/en/latest/pygod.metrics.html#pygod.metrics.eval_ndcg).

What's New
* Our paper is available on [arXiv](https://arxiv.org/abs/2204.12095).
* We enable most of the models to train with minbatch, see [model list](https://docs.pygod.org/en/latest/#implemented-algorithms) for supported models. kayzliu xyvivian aha12345678
* Add new models [CoLA](https://docs.pygod.org/en/latest/pygod.models.html#cola) (beta) and ANEMONE (beta) by harvardchen
* The first community contributor zhiming-xu add a new model [CONAD](https://docs.pygod.org/en/latest/pygod.models.html#conad).
* Add new metric [eval_average_precision](https://docs.pygod.org/en/latest/pygod.utils.html#pygod.utils.metric.eval_average_precision) by YingtongDou.
* Improved device setting by yzhao062