DGL: Impactful graph neural networks: A Tale of Research and Productionization

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 productionization. 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 handle such challenges and integrate those into 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 productionizing graph machine learning (GML). Neptune ML facilitates this goal and helps customers obtain real-time GNN predictions with graph databases using graph query languages. At Amazon and AWS we develop frameworks based on DGL to solve internal and external GML problems and realize the impact of GNNs. Bio: Vassilis N. Ioannidis is an Applied Scientist in AWS AI Research and Education (AIRE). He received his Ph.D. degree for his dissertation in “Robust Deep Learning on Graphs” from the University of Minnesota (UMN), Twin Cities, Minneapolis, MN, USA, in 2020. He also received the Doctoral Dissertation Fellowship as a recognition for his graph representation learning research. Vassilis has published more than 40 conference and journal papers. He worked from June to December 2019 at Mitsubishi Electric research labs in graph representation learning. Since February 2020, he is working at AWS in the deep graph library team, where he develops GNN solutions and performs research in GNNs. He worked on developing Neptune ML which is a machine learning service over graph databases deployed in AWS using GNNs. He also works in large-scale training of superposition of language models and GNNs for Amazon projects in information retrieval, recommendation, and abuse detection.