‘AutoGL’, a new AutoML framework for Graphs by Tsinghua University
‘Graphs’ and linked data are highly useful in generating effective recommender systems. Also in machine learning, graphs have gained popularity.
Now, researchers from Tsinghua University (China) have released AutoGL (GitHub), the first AutoML tool for graph datasets and tasks. This tool could greatly advance the ease of working with graph data and algorithms for both machine learning and recommender systems.
AutoGL (i.e. Auto Graph Learning) is an automatic machine learning (AutoML) toolkit specified for graph datasets & tasks.
It will automatically handle all the stages involved in graph learning problems, including dataset download & management, data preprocessing and feature engineering, model selection and training, hyper-parameter tuning and ensemble, which will reduce human labors and biases in the machine learning loop by a large scale. This toolkit also serves as a platform for users to implement and test their own auto or graph learning methods. The workflow below gives the overall framework of AutoGL.
http://mn.cs.tsinghua.edu.cn/autogl/
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About The Author
Joeran Beel
I am the founder of Recommender-Systems.com and head of the Intelligent Systems Group (ISG) at the University of Siegen, Germany https://isg.beel.org. We conduct research in recommender-systems (RecSys), personalization and information retrieval (IR) as well as on automated machine learning (AutoML), meta-learning and algorithm selection. Domains we are particularly interested in include smart places, eHealth, manufacturing (industry 4.0), mobility, visual computing, and digital libraries. We founded or maintain, among others, LensKit-Auto, Darwin & Goliath, Mr. DLib, and Docear, each with thousand of users; we contributed to TensorFlow, JabRef and others; and we developed the first prototypes of automated recommender systems (AutoSurprise and Auto-CaseRec) and Federated Meta Learning (FMLearn Server and Client).