A Python Library for Graph Outlier Detection (Anomaly Detection)
GADNR
by @YingtongDou and DMGD
by @kayzliuGreatly 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:
detector.fit
, detector.predict
New features in recent versions:
utils.load_data
nn.encoder
, nn.decoder
, nn.fuctional
, etc.metric
, generator
, utils
, etc.If you encounter a bug or have any suggestions please fill an issue or reach us via email at [email protected]. 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:
Detector
: base class for all detectors.DeepDetector
: base class for all deep learning based detectors.predict_proba
and predict_confidence
.predict(return_prob=True, return_conf=True)
instead.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
to_edge_score
: edge outlier score converterto_graph_score
: graph outlier score converterinit_detector
: detector initializerinit_nn
: neural network initializerdetector(compile_model=True)
(beta)MLPAE
and GCNAE
to GAE
save_emb=True
by detector.emb
CoLA
(beta) and ANEMONE
(beta) by @harvardchenCONAD
.eval_average_precision
by @YingtongDou.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 and PyG. It features:
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 or reach us via email [email protected]. Also, feel free to try it out with your code!
We appreciate every star, fork, and follow.