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.. figure:: pygod_logo.png
    :scale: 30%
    :alt: logo

----

|badge_pypi| |badge_docs| |badge_stars| |badge_forks| |badge_downloads| |badge_testing| |badge_coverage| |badge_license|

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----

PyGOD is a **Python library** for **graph outlier detection** (anomaly detection).
This exciting yet challenging field has many key applications, e.g., detecting
suspicious activities in social networks :cite:`dou2020enhancing`  and security systems :cite:`cai2021structural`.

PyGOD includes **10+** graph outlier detection algorithms.
For consistency and accessibility, PyGOD is developed on top of `PyTorch Geometric (PyG) <https://www.pyg.org/>`_
and `PyTorch <https://pytorch.org/>`_, and follows the API design of `PyOD <https://github.com/yzhao062/pyod>`_.
See examples below for detecting outliers with PyGOD in 5 lines!


**PyGOD is featured for**:

* **Unified APIs, detailed documentation, and interactive examples** across various graph-based algorithms.
* **Comprehensive coverage** of 10+ graph outlier detectors.
* **Full support of detections at multiple levels**, such as node-, edge-, and graph-level tasks.
* **Scalable design for processing large graphs** via mini-batch and sampling.
* **Streamline data processing with PyG**--fully compatible with PyG data objects.

**Outlier Detection Using PyGOD with 5 Lines of Code**\ :


.. code-block:: python


    # train a dominant detector
    from pygod.detector import DOMINANT

    model = DOMINANT(num_layers=4, epoch=20)  # hyperparameters can be set here
    model.fit(train_data)  # input data is a PyG data object

    # get outlier scores on the training data (transductive setting)
    score = model.decision_score_

    # predict labels and scores on the testing data (inductive setting)
    pred, score = model.predict(test_data, return_score=True)

----


Implemented Algorithms
----------------------

==================  =====  ===========  ===========  ==============================================
Abbr                Year   Backbone     Sampling     Class
==================  =====  ===========  ===========  ==============================================
SCAN                2007   Clustering   No           :class:`pygod.detector.SCAN`
GAE                 2016   GNN+AE       Yes          :class:`pygod.detector.GAE`
Radar               2017   MF           No           :class:`pygod.detector.Radar`
ANOMALOUS           2018   MF           No           :class:`pygod.detector.ANOMALOUS`
ONE                 2019   MF           No           :class:`pygod.detector.ONE`
DOMINANT            2019   GNN+AE       Yes          :class:`pygod.detector.DOMINANT`
DONE                2020   MLP+AE       Yes          :class:`pygod.detector.DONE`
AdONE               2020   MLP+AE       Yes          :class:`pygod.detector.AdONE`
AnomalyDAE          2020   GNN+AE       Yes          :class:`pygod.detector.AnomalyDAE`
GAAN                2020   GAN          Yes          :class:`pygod.detector.GAAN`
DMGD                2020   GNN+AE       Yes          :class:`pygod.detector.DMGD`
OCGNN               2021   GNN          Yes          :class:`pygod.detector.OCGNN`
CoLA                2021   GNN+AE+SSL   Yes          :class:`pygod.detector.CoLA`
GUIDE               2021   GNN+AE       Yes          :class:`pygod.detector.GUIDE`
CONAD               2022   GNN+AE+SSL   Yes          :class:`pygod.detector.CONAD`
GADNR               2024   GNN+AE       Yes          :class:`pygod.detector.GADNR`
==================  =====  ===========  ===========  ==============================================


----


.. toctree::
   :maxdepth: 2
   :hidden:
   :caption: Getting Started

   install
   tutorials/index
   api_cc
   minibatch

.. toctree::
   :maxdepth: 3
   :hidden:
   :caption: API References

   pygod.detector
   pygod.generator
   pygod.metric
   pygod.nn
   pygod.nn.conv
   pygod.nn.encoder
   pygod.nn.decoder
   pygod.nn.functional
   pygod.utils

.. toctree::
   :maxdepth: 2
   :hidden:
   :caption: Additional Information

   cite
   team
   reference