# pygod-team/pygod

A Python Library for Graph Outlier Detection (Anomaly Detection)

Repository: https://github.com/pygod-team/pygod
Canonical: https://ross.abutalabs.com/products/pygod
Homepage: https://pygod.org
Language: Python
License: BSD-2-Clause
License Family: permissive
Topics: outlier-detection, anomaly-detection, graph-anomaly-detection, machine-learning, security-tools, opensource, deeplearning, python, graphmining, pytorch, graph-neural-networks, fraud-detection, toolkit
Last push: 2024-11-14T06:35:15+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 1749, "days_push": 657, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1496, forks 139 (observed 2026-08-28T04:04:53.625491+00:00)

## What it is
PyGOD is a Python library for graph outlier detection (anomaly detection) built on PyTorch and PyTorch Geometric. It provides 10+ graph-based anomaly detection algorithms with a unified API following the PyOD design, supporting node-, edge-, and graph-level detection.

## Use cases
- detect anomalies in graph-structured data
- find fraudulent accounts in social or transaction networks
- detect suspicious nodes in security systems
- run graph neural network based outlier detection in Python
- benchmark graph anomaly detection algorithms
- scale outlier detection to large graphs with mini-batching

## When to choose
- your data is naturally graph-structured and you need anomaly detection
- you want a scikit-learn/PyOD-style unified API over multiple graph detectors
- you already use PyTorch Geometric and want compatible data handling
- you need node-, edge-, or graph-level detection in one toolkit

## When to avoid
- your data is tabular with no graph structure (use PyOD instead)
- you need a production serving system rather than a modeling library
- you work outside the PyTorch ecosystem, e.g. TensorFlow or JAX

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, data-science
- domain: machine-learning, security, data-science
- platform: python, cross-platform
- tags: anomaly-detection, outlier-detection, graph-neural-networks, pytorch, pytorch-geometric, fraud-detection, graph-mining, algorithms

## Member repositories
- pygod-team/pygod (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:53.625491+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:33:14.623583+00:00, confidence not recorded.
  - readme: https://github.com/pygod-team/pygod (fetched 2026-08-28T04:04:53.625491+00:00, sha 0a306fd8e780)
  - homepage: https://pygod.org (fetched 2026-08-29T11:38:32.609786+00:00, sha b228bcb52a2a)
  - registry_pypi: https://pypi.org/pypi/pygod/json (fetched 2026-08-29T11:38:32.612921+00:00, sha 21998795455c)
- Data as of 2026-08-30T08:39:29.467469+00:00.