Karate Club DocumentationΒΆ

Karate Club is an unsupervised machine learning extension library for NetworkX. It builds on other open source linear algebra, machine learning, and graph signal processing libraries such as Numpy, Scipy, Gensim, PyGSP, and Scikit-Learn. Karate Club consists of state-of-the-art methods to do unsupervised learning on graph structured data. To put it simply it is a Swiss Army knife for small-scale graph mining research. First, it provides network embedding techniques at the node and graph level. Second, it includes a variety of overlapping and non-overlapping commmunity detection methods. Implemented methods cover a wide range of network science (NetSci, Complenet), data mining (ICDM, CIKM, KDD), artificial intelligence (AAAI, IJCAI) and machine learning (NeurIPS, ICML, ICLR) conferences, workshops, and pieces from prominent journals.

>@inproceedings{karateclub,
                title = {{Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs}},
                author = {Benedek Rozemberczki and Oliver Kiss and Rik Sarkar},
                year = {2020},
                booktitle = {Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM '20)},
                organization = {ACM},
 }