Graph neural networks (GNNs) learn from complex graph data and have been remarkably successful in various applications and across industries. Furthering the impact of GNNs entails solving challenges related to modeling and scalability research and productization. Impactful GNN research requires constant innovation to handle rich, time-evolving and heterogenous graph data as well as trillion-edge scale graphs. We develop GNN models and distributed training techniques to address these challenges and integrate the solutions to the deep graph library (DGL). DGL is a scalable and widely adopted library for developing GNN models. Building GNN products requires domain expertise and significant effort. At AWS we aim at lowering the bar in productizing graph machine learning (GML). Neptune ML facilitates this goal and helps customers obtain real time GNN predictions with graph databases using graph query languages. Amazon develops frameworks based on DGL to solve internal and external GML problems and realize the impact of GNNs.
Join us at the NeurIPS 2022 located in New Orleans Ernest N. Morial Convention Center on Monday, November 28 at 2:00pm in Room 291 regarding the following exciting topics.