Learning Graph Neural Networks with Deep Graph Library [conference paper]

Conference

Companion Proceedings of the Web Conference - April 20, 2020

Authors

Da Zheng, Minjie Wang, Quan Gan, Zheng Zhang, George Karypis (professor)

Abstract

Learning from graph and relational data plays a major role in many applications including social network analysis, marketing, e-commerce, information retrieval, knowledge modeling, medical and biological sciences, engineering, and others. In the last few years, Graph Neural Networks (GNNs) have emerged as a promising new supervised learning framework capable of bringing the power of deep representation learning to graph and relational data. This ever-growing body of research has shown that GNNs achieve state-of-the-art performance for problems such as link prediction, fraud detection, target-ligand binding activity prediction, knowledge-graph completion, and product recommendations.

Link to full paper

Learning Graph Neural Networks with Deep Graph Library

Keywords

graph neural networks