  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.
