Update README.md

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 community 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.

Author: benedekrozemberczki

README.md

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 * [RuleFit](https://github.com/christophM/rulefit) - Implementation of the rulefit. <img height="20" src="img/sklearn_big.png" alt="sklearn">
 * [metric-learn](https://github.com/all-umass/metric-learn) - Metric learning algorithms in Python. <img height="20" src="img/sklearn_big.png" alt="sklearn">
 * [pyGAM](https://github.com/dswah/pyGAM) - Generalized Additive Models in Python.
+* [Karate Club](https://github.com/benedekrozemberczki/karateclub) - An unsupervised machine learning library for graph structured data.
 
 ### Time Series
 * [tslearn](https://github.com/rtavenar/tslearn) - Machine learning toolkit dedicated to time-series data. <img height="20" src="img/sklearn_big.png" alt="sklearn">