论文标题
空手道俱乐部：面向API的开源Python框架，用于无监督的学习
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs
论文作者
论文摘要
我们向空手道俱乐部提出了一个Python框架，结合了30多种最先进的图形挖掘算法，可以解决无监督的机器学习任务。该包装的主要目标是使社区检测，节点和整个图形嵌入到许多机器学习研究人员和从业人员中。我们设计了空手道俱乐部，重点是一致的应用程序接口，可扩展性，易用性，明智的框模型行为，标准化的数据集摄入和输出生成。本文通过实例讨论了该框架背后的设计原理。我们显示了空手道俱乐部在学习绩效方面的效率，这些效率在各种现实世界的聚类问题，分类任务和支持证据方面的竞争速度方面的效率。
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.
