Large-scale graph neural networks for real-world industrial applications

Abstract

A graph or network is a powerful data structure that can represent relationships between any entities in both the digital world and the physical world. The way of analyzing graphs has been advancing from algorithm-based approaches to datadriven approaches with machine learning and neural networks just like other types of data such as text, image, and speech. In this talk, I will describe how graph neural networks have emerged as a powerful learning tool that backs up conventional graph algorithm-based approaches, and also introduce our ongoing research projects and collaborations with industry around graph neural networks such as recommendation. I will briefly introduce a nationwide cloud computing project called “mdx” as well as a nationwide materials informatics project named ARIM (Advanced Research Infrastructure for Materials and Nanotechnology).