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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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 publicationProceedings of the 29th ACM International Conference on Information & Knowledge Management
Place of PublicationNew York, NY, USA
PublisherAssociation for Computing Machinery (ACM)
Pages3125–3132
ISBN (Print)9781450368599
DOIs
Publication statusPublished - 19 Oct 2020
Event29th ACM International Conference on Information and Knowledge Management - Omline Conference
Duration: 19 Oct 202023 Oct 2020
https://www.cikm2020.org/index.html

Conference

Conference29th ACM International Conference on Information and Knowledge Management
Abbreviated titleCIKM 2020
CityOmline Conference
Period19/10/2023/10/20
Internet address

Keywords

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

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