GraphStorm: all-in-one graph machine learning framework for industry applications

TL;DR

GraphStorm is an all-in-one, scalable graph machine learning framework designed for industry applications, enabling easy, expert-friendly, and large-scale graph modeling and inference with minimal effort.

Contribution

It introduces a comprehensive, scalable GML framework that simplifies deployment in industry, supporting billion-scale graphs with a single command and advanced modeling techniques.

Findings

Deployed in over a dozen billion-scale industry applications

Supports graphs with billions of nodes and scalable training/inference

Open-sourced for community use and development

Abstract

Graph machine learning (GML) is effective in many business applications. However, making GML easy to use and applicable to industry applications with massive datasets remain challenging. We developed GraphStorm, which provides an end-to-end solution for scalable graph construction, graph model training and inference. GraphStorm has the following desirable properties: (a) Easy to use: it can perform graph construction and model training and inference with just a single command; (b) Expert-friendly: GraphStorm contains many advanced GML modeling techniques to handle complex graph data and improve model performance; (c) Scalable: every component in GraphStorm can operate on graphs with billions of nodes and can scale model training and inference to different hardware without changing any code. GraphStorm has been used and deployed for over a dozen billion-scale industry applications after its release in May 2023. It is open-sourced in Github: https://github.com/awslabs/graphstorm.