Summary of Haste Makes Waste: a Simple Approach For Scaling Graph Neural Networks, by Rui Xue et al.
Haste Makes Waste: A Simple Approach for Scaling Graph Neural Networks
by Rui Xue, Tong Zhao, Neil Shah, Xiaorui Liu
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 limitations of Graph Neural Networks (GNNs) when dealing with large-scale graphs. Specifically, it examines training algorithms that utilize historical embeddings to reduce computation and memory costs while maintaining expressiveness. However, these approaches incur significant computation bias due to stale feature history. The authors analyze this staleness and its impact on performance, finding inferior results on large-scale problems. They propose a new algorithm (REST) that effectively reduces feature staleness, leading to improved performance and convergence across varying batch sizes. REST integrates seamlessly with existing solutions, boasting easy implementation and superior performance and efficiency on large-scale benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making Graph Neural Networks better for really big graphs. Right now, these networks are great at learning from small graphs, but they don’t do as well when dealing with huge amounts of data. The problem is that the older information gets stuck in memory, making it harder to learn from new data. To fix this, the authors came up with a new way to train the network called REST. This method helps get rid of old information and makes the network work better on big datasets. It’s easy to use and does a great job. |
