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. Recently, Graph Neural Networks (GNNs) have emerged as a promising new 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. In practice, many of the real-world graphs are very large. It is urgent to have scalable solutions to train GNN on large graphs efficiently. 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 scalable GNN framework that simplifies the development of efficient GNN-based training and inference programs at a large scale. To make things concrete, the tutorial will cover state-of-the-art training methods to scale GNN to large graphs and provide hands-on sessions to show how to use DGL to perform scalable training in different settings (multi-GPU training and distributed training). This hands-on part will start with basic graph applications (e.g., node classification and link prediction) to set up the context and move on to train GNNs on large graphs. It will provide tutorials to demonstrate how to apply the techniques in DGL to train GNNs for real-world applications.
Original language | English (US) |
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Title of host publication | WSDM 2021 - Proceedings of the 14th ACM International Conference on Web Search and Data Mining |
Publisher | Association for Computing Machinery, Inc |
Pages | 1141-1142 |
Number of pages | 2 |
ISBN (Electronic) | 9781450382977 |
DOIs | |
State | Published - Aug 3 2021 |
Externally published | Yes |
Event | 14th ACM International Conference on Web Search and Data Mining, WSDM 2021 - Virtual, Online, Israel Duration: Mar 8 2021 → Mar 12 2021 |
Publication series
Name | WSDM 2021 - Proceedings of the 14th ACM International Conference on Web Search and Data Mining |
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Conference
Conference | 14th ACM International Conference on Web Search and Data Mining, WSDM 2021 |
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Country/Territory | Israel |
City | Virtual, Online |
Period | 3/8/21 → 3/12/21 |
Bibliographical note
Publisher Copyright:© 2021 ACM.
Keywords
- deep graph library
- graph neural networks
- scalability