Summary of Graph Neural Networks Gone Hogwild, by Olga Solodova et al.
Graph Neural Networks Gone Hogwild
by Olga Solodova, Nick Richardson, Deniz Oktay, Ryan P. Adams
First submitted to arxiv on: 29 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 A novel paper explores the limitations of message passing graph neural networks (GNNs) when used in asynchronous inference scenarios. These architectures typically excel in learning distributed algorithms via gradient descent but fail catastrophically in asynchrony, rendering them unsuitable for applications like robotic swarms or sensor networks. The authors identify a class of implicitly-defined GNNs that are robust to partially asynchronous “hogwild” inference and propose an energy GNN architecture that outperforms others on synthetic tasks inspired by multi-agent systems, while achieving competitive performance on real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new paper looks at why graph neural networks (GNNs) don’t work well when used in a certain way. Normally, GNNs are great at learning distributed algorithms, but they make mistakes when different parts of the network update information at different times. This makes them not very useful for things like controlling robots or sensors. The paper finds that some types of GNNs can work even if the updates happen slightly out of sync and proposes a new kind of GNN that does better on test problems than other similar ones. |
Keywords
» Artificial intelligence » Gnn » Gradient descent » Inference
