# Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs

arXiv: https://arxiv.org/abs/2003.04819 · HTML: https://mlsanity.com/paper/2003.04819/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2020.

## Built on by (most cited first, 7 of 7)

- [Graph Contrastive Learning Automated](https://mlsanity.com/paper/2106.07594/) (2021)
- [A Comprehensive Review of Community Detection in Graphs](https://mlsanity.com/paper/2309.11798/) (2023)
- [Evaluating Document Representations for Content-based Legal Literature Recommendations](https://mlsanity.com/paper/2104.13841/) (2021)
- [Edge but not Least: Cross-View Graph Pooling](https://mlsanity.com/paper/2109.11796/) (2021)
- [EPIC: Graph Augmentation with Edit Path Interpolation via Learnable Cost](https://mlsanity.com/paper/2306.01310/) (2023)
- [Learning-Based Heuristic for Combinatorial Optimization of the Minimum Dominating Set Problem using Graph Convolutional Networks](https://mlsanity.com/paper/2306.03434/) (2023)
- [Climate Intervention Analysis using AI Model Guided by Statistical Physics Principles](https://mlsanity.com/paper/2302.03258/) (2023)

## Abstract (arXiv)

We present Karate Club a Python framework combining more than 30 state-of-the-art graph mining algorithms which can solve unsupervised machine learning tasks. The primary goal of the package is to make community detection, node and whole graph embedding available to a wide audience of machine learning researchers and practitioners. We designed Karate Club with an emphasis on a consistent application interface, scalability, ease of use, sensible out of the box model behaviour, standardized dataset ingestion, and output generation. This paper discusses the design principles behind this framework with practical examples. We show Karate Club's efficiency with respect to learning performance on a wide range of real world clustering problems, classification tasks and support evidence with regards to its competitive speed.