arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2003.04819cs.LGcs.SIstat.ML

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

The University of Edinburgh(爱丁堡大学);Central European University(中欧大学)

Benedek Rozemberczki, Oliver Kiss, Rik Sarkar

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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