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Graph Neural Network Research at AWS AI

Published: 15 February 2022 Publication History

Abstract

In the course of just a few years, Graph Neural Networks (GNNs) have emerged as the prominent supervised learning approach that brings the power of deep representation learning to graph and relational data. An 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. As a result, GNNs are quickly moving from the realm of academic research involving small graphs to powering commercial applications and very large graphs. This talk will provide an overview of some of the research that AWS AI has been doing to facilitate this transition, which includes developing the Deep Graph Library (DGL)-an open source framework for writing and training GNN-based models, improving the computational efficiency and scaling of GNN model training for extremely large graphs, developing novel GNN-based solutions for different applications, and making it easy for developers to train and use GNN models by integrating graph-based ML techniques in graph databases.

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MP4 File (Keynote1.mp4)
WSDM'22 Keynote "Graph Neural Network Research at AWS AI"

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cover image ACM Conferences
WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining
February 2022
1690 pages
ISBN:9781450391320
DOI:10.1145/3488560
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Association for Computing Machinery

New York, NY, United States

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Published: 15 February 2022

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