Summary of Graphstorm: All-in-one Graph Machine Learning Framework For Industry Applications, by Da Zheng et al.
GraphStorm: all-in-one graph machine learning framework for industry applications
by Da Zheng, Xiang Song, Qi Zhu, Jian Zhang, Theodore Vasiloudis, Runjie Ma, Houyu Zhang, Zichen Wang, Soji Adeshina, Israt Nisa, Alejandro Mottini, Qingjun Cui, Huzefa Rangwala, Belinda Zeng, Christos Faloutsos, George Karypis
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The GraphStorm system, released in May 2023, offers an end-to-end solution for scalable graph construction, model training, and inference. This system provides desirable properties such as ease of use, expert-friendliness, and scalability. It can operate on massive datasets with billions of nodes and scale model training and inference without code changes. GraphStorm has been used in over a dozen billion-scale industry applications since its release. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GraphStorm is a new tool that helps make graph machine learning (GML) easier to use for many industries. GML is good at solving certain problems, but it can be hard to set up and use with very large datasets. GraphStorm makes this process simple by having just one command to do all the work. It also has advanced techniques built in to help improve model performance on complex data. The system can handle massive datasets and scale its processing power without changing any code. So far, it has been used in many industries and has had a big impact. |
Keywords
» Artificial intelligence » Inference » Machine learning
