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.
|Original language||English (US)|
|Title of host publication||WSDM 2022 - Proceedings of the 15th ACM International Conference on Web Search and Data Mining|
|Publisher||Association for Computing Machinery, Inc|
|State||Published - Feb 11 2022|
|Event||15th ACM International Conference on Web Search and Data Mining, WSDM 2022 - Virtual, Online, United States|
Duration: Feb 21 2022 → Feb 25 2022
|Name||WSDM 2022 - Proceedings of the 15th ACM International Conference on Web Search and Data Mining|
|Conference||15th ACM International Conference on Web Search and Data Mining, WSDM 2022|
|Period||2/21/22 → 2/25/22|
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- Keynote Talk