Representation Learning on Networks: Theories, Algorithms, and Applications

Abstract

Network representation learning offers a revolutionary paradigm for mining and learning with network data. In this tutorial, we will give a systematic introduction for representation learning on networks. We will start the tutorial with industry examples from Alibaba, AMiner, Microsoft Academic, WeChat, and XueTangX to explain how network analysis and graph mining on the Web are benefiting from representation learning. Then we will comprehensively introduce both the history and recent advances on network representation learning, such as network embedding and graph neural networks. Uniquely, this tutorial aims to provide the audience with the underlying theories in network representation learning, as well as our experience in translating this line of research into real-world applications on the Web. Finally, we will release public datasets and benchmarks for open and reproducible network representation learning research. The tutorial accompanying page is at https://aminer.org/nrl_www2019.