# benedekrozemberczki/karateclub

Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)

Repository: https://github.com/benedekrozemberczki/karateclub
Canonical: https://ross.abutalabs.com/products/karateclub
Homepage: https://karateclub.readthedocs.io
Language: Python
License: GPL-3.0
License Family: copyleft
Topics: community-detection, graph-clustering, deepwalk, networkx, louvain, network-science, machine-learning, unsupervised-learning, gcn, node2vec, graph2vec, supervised-learning, sklearn, networkx-graph, scikit, label-propagation, graph-embedding, network-embedding, node-embedding, 2vec
Last push: 2024-07-17T19:00:35+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": 2463, "days_push": 777, "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 2286, forks 255 (observed 2026-08-28T04:06:34.086919+00:00)

## What it is
Karate Club is a Python library of unsupervised machine learning algorithms for graph-structured data, built as an extension of NetworkX. It provides node and graph embedding techniques plus overlapping and non-overlapping community detection methods with a scikit-learn-like API.

## Use cases
- embed nodes of a networkx graph with node2vec or deepwalk
- detect communities in a network using louvain or label propagation
- compute graph-level embeddings for graph classification
- cluster a graph with unsupervised learning methods
- do small-scale graph mining research in python
- learn node representations for downstream machine learning tasks

## When to choose
- you work with NetworkX graphs and want a consistent sklearn-style API for graph ML
- you need many community detection or graph embedding algorithms in one library
- you are doing small-scale graph mining or network science research

## When to avoid
- you need large-scale, distributed graph processing on billions of edges
- you need supervised graph neural network training frameworks like PyTorch Geometric
- you require a permissive license since karateclub is GPL-3.0

## Facets
- artifact type: library
- maturity: stable
- function: machine-learning, data-science, nlp
- domain: machine-learning, data-science, graph-processing
- platform: python
- tags: graph-embedding, community-detection, networkx, node2vec, deepwalk, graph2vec, louvain, unsupervised-learning, graph-mining, network-science, algorithms

## Member repositories
- benedekrozemberczki/karateclub (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:34.086919+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 2026-08-30T02:40:53.468207+00:00, confidence not recorded.
  - readme: https://github.com/benedekrozemberczki/karateclub (fetched 2026-08-28T04:06:34.086919+00:00, sha 088b5446cd6a)
  - registry_pypi: https://pypi.org/pypi/karateclub/json (fetched 2026-08-29T10:21:40.432215+00:00, sha 80ce5a5d4c77)
- Data as of 2026-08-30T08:39:29.467469+00:00.