Scalable Graph Neural Networks with Deep Graph Library [conference paper]

Conference

26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining - August 23, 2020

Authors

Da Zheng, Minjie Wang, Quan Gan, Zheng Zhang, George Karypis (professor)

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. In the last few years, Graph Neural Networks (GNNs) have emerged as a promising new supervised 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.

Link to full paper

Scalable Graph Neural Networks with Deep Graph Library

Keywords

graph neural network, data mining