Karate Club swMATH ID: 32339 Software Authors: Benedek Rozemberczki, Oliver Kiss, Rik Sarkar Description: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs. 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. Homepage: https://arxiv.org/abs/2003.04819 Dependencies: Python Keywords: Machine Learning; arXiv_cs.LG; Social and Information Networks; arXiv_cs.SI; arXiv_stat.ML; Python; Unsupervised Learning; Graphs Related Software: Scikit; NetLSD; RolX; node2vec; GraRep; PyGSP; NumPy; FSCNMF; NodeSketch; GL2vec; SINE; SciPy; PyTorch; TensorFlow; graph2vec; Graph-tool; NetworkX; SNAP; Python; word2vec Cited in: 1 Publication Standard Articles 1 Publication describing the Software Year An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs Benedek Rozemberczki, Oliver Kiss, Rik Sarkar 2020 Cited by 3 Authors 1 Chazal, Frédéric 1 Levrard, Clément 1 Royer, Martin Cited in 1 Serial 1 Electronic Journal of Statistics Cited in 2 Fields 1 Probability theory and stochastic processes (60-XX) 1 Statistics (62-XX) Citations by Year