pyod and pygod
These are complements: PyOD provides general-purpose outlier detection algorithms (tabular, time-series, neural network-based) while PyGOD specializes in detecting anomalies specifically within graph-structured data, so they address different data modalities and can be used together in pipelines that process both relational and graph-based features.
About pyod
yzhao062/pyod
A Python Library for Outlier and Anomaly Detection, Integrating Classical and Deep Learning Techniques
Provides 45+ detection algorithms unified under a scikit-learn compatible API, combining classical methods (LOF, Isolation Forest) with 12 PyTorch-based neural models. Emphasizes performance through Numba JIT compilation and the SUOD framework for fast training/prediction, plus LLM-guided automated model selection to reduce manual hyperparameter tuning.
About pygod
pygod-team/pygod
A Python Library for Graph Outlier Detection (Anomaly Detection)
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work