@inproceedings{3fe5560af1fe42b5a7c79e9c8ff71844,
title = "Karate Club: An API Oriented Open-Source Python Framework for Unsupervised Learning on Graphs",
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.",
keywords = "community detection, graph classification, graph embedding, graph mining, machine learning, network embedding, node embedding",
author = "Benedek Rozemberczki and Oliver Kiss and Rik Sarkar",
note = "Publisher Copyright: {\textcopyright} 2020 ACM.; 29th ACM International Conference on Information and Knowledge Management, CIKM 2020 ; Conference date: 19-10-2020 Through 23-10-2020",
year = "2020",
month = oct,
day = "19",
doi = "10.1145/3340531.3412757",
language = "English",
series = "International Conference on Information and Knowledge Management, Proceedings",
publisher = "Association for Computing Machinery",
pages = "3125--3132",
editor = "Mathieu d'Aquin and Stefan Dietze",
booktitle = "CIKM 2020: Proceedings of the 29th ACM International Conference on Information and Knowledge Management",
}