Learning Graph Neural Networks with Deep Graph Library

Da Zheng, Minjie Wang, Quan Gan, Zheng Zhang, George Karypis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations


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. The objective of this tutorial is twofold. First, it will provide an overview of the theory behind GNNs, discuss the types of problems that GNNs are well suited for, and introduce some of the most widely used GNN model architectures and problems/applications that are designed to solve. Second, it will introduce the Deep Graph Library (DGL), a new software framework that simplifies the development of efficient GNN-based training and inference programs. To make things concrete, the tutorial will provide hands-on sessions using DGL. This hands-on part will cover both basic graph applications (e.g., node classification and link prediction), as well as more advanced topics including training GNNs on large graphs and in a distributed setting. In addition, it will provide hands-on tutorials on using GNNs and DGL for real-world applications such as recommendation and fraud detection.

Original languageEnglish (US)
Title of host publicationThe Web Conference 2020 - Companion of the World Wide Web Conference, WWW 2020
PublisherAssociation for Computing Machinery
Number of pages2
ISBN (Electronic)9781450370240
StatePublished - Apr 20 2020
Event29th International World Wide Web Conference, WWW 2020 - Taipei, Taiwan, Province of China
Duration: Apr 20 2020Apr 24 2020

Publication series

NameThe Web Conference 2020 - Companion of the World Wide Web Conference, WWW 2020


Conference29th International World Wide Web Conference, WWW 2020
Country/TerritoryTaiwan, Province of China

Bibliographical note

Publisher Copyright:
© 2020 ACM.


  • Deep Graph Library
  • applications
  • graph neural networks


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