Summary of Rehub: Linear Complexity Graph Transformers with Adaptive Hub-spoke Reassignment, by Tomer Borreda et al.
ReHub: Linear Complexity Graph Transformers with Adaptive Hub-Spoke Reassignment
by Tomer Borreda, Daniel Freedman, Or Litany
First submitted to arxiv on: 2 Dec 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 novel graph transformer architecture, ReHub, presents an efficient reassignment technique between nodes and virtual nodes to achieve linear complexity. This approach addresses limitations in message-passing graph networks, such as oversmoothing and oversquashing. Inspired by the airline industry’s hub-and-spoke model, ReHub dynamically reassigns graph nodes to a fixed number of virtual nodes at each model layer. The adaptive reassignment technique eliminates expensive node-hub computations while leveraging all hubs without increasing complexity. Experiments on LRGB demonstrate consistent improvements over baselines, with performance on par with non-sparse models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ReHub is a new way to process big graphs. It’s like an airport, where flights (graph nodes) are assigned to optimize how they communicate. This helps solve problems in old graph processing methods. The new approach uses fewer “airports” (virtual nodes) and reassigns the flights more efficiently. This makes it faster and better than before. Tests on a big graph show that ReHub does well and is as good as older methods. |
Keywords
» Artificial intelligence » Transformer
