Summary of Graph Neural Network Training Systems: a Performance Comparison Of Full-graph and Mini-batch, by Saurabh Bajaj and Hojae Son and Juelin Liu and Hui Guan and Marco Serafini
Graph Neural Network Training Systems: A Performance Comparison of Full-Graph and Mini-Batch
by Saurabh Bajaj, Hojae Son, Juelin Liu, Hui Guan, Marco Serafini
First submitted to arxiv on: 1 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 paper explores the differences between two methods of training Graph Neural Networks (GNNs): mini-batch training and full-graph training. Two distinct classes of GNN training systems emerged, each optimized for one method, with limited comparison between them. Some prior work justifies its focus on a specific training method by claiming higher accuracy, but the literature lacks complete and consistent evidence. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how we train Graph Neural Networks (GNNs) in two different ways: mini-batch training and full-graph training. Because these methods require different tools and tricks, two types of systems for training GNNs were developed. Some people think one way is better than the other because it gets more accurate results. But really, we don’t know which one is best. |
Keywords
» Artificial intelligence » Gnn
