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

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Authors Oliver Kiss, Rik Sarkar, Benedek Rozemberczki
Journal/Conference Name CIKM 2020 10
Paper Category
Paper Abstract 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.
Date of publication 2020
Code Programming Language Multiple
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