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

Benedek Rozemberczki, Oliver Kiss, Rik Sarkar

Research output: Contribution to Book/Report typesConference contributionpeer-review

Abstract (may include machine translation)

Graphs encode important structural properties of complex systems. Machine learning on graphs has therefore emerged as an important technique in research and applications. We present Karate Club - a Python framework combining more than 30 state-of-the-art graph mining algorithms. These unsupervised techniques make it easy to identify and represent common graph features. 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. Karate Club is designed 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 the framework with practical examples. We show Karate Club's efficiency in learning performance on a wide range of real world clustering problems and classification tasks along with supporting evidence of its competitive speed.

Original languageEnglish
Title of host publicationCIKM 2020: Proceedings of the 29th ACM International Conference on Information and Knowledge Management
EditorsMathieu d'Aquin, Stefan Dietze
PublisherAssociation for Computing Machinery
Pages3125-3132
Number of pages8
ISBN (Electronic)9781450368599
DOIs
StatePublished - 19 Oct 2020
Event29th ACM International Conference on Information and Knowledge Management, CIKM 2020 - Virtual, Online, Ireland
Duration: 19 Oct 202023 Oct 2020

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Conference

Conference29th ACM International Conference on Information and Knowledge Management, CIKM 2020
Country/TerritoryIreland
CityVirtual, Online
Period19/10/2023/10/20

Keywords

  • community detection
  • graph classification
  • graph embedding
  • graph mining
  • machine learning
  • network embedding
  • node embedding

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