Summary of Polyformer: Scalable Node-wise Filters Via Polynomial Graph Transformer, by Jiahong Ma et al.
PolyFormer: Scalable Node-wise Filters via Polynomial Graph Transformer
by Jiahong Ma, Mingguo He, Zhewei Wei
First submitted to arxiv on: 19 Jul 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 proposes a new method for learning node-wise filters in graph representation learning, called PolyAttn. This approach leverages the attention mechanism to directly learn node-wise filters, which offers powerful representation capabilities and is scalable on large-scale graphs. The model, named PolyFormer, combines PolyAttn with Graph Transformer models and captures spectral information, enhancing expressiveness while maintaining efficiency. The authors demonstrate that their proposed methods excel at learning arbitrary node-wise filters and show superior performance on both homophilic and heterophilic graphs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PolyAttn is a new approach to learning node-wise filters in graph representation learning. It uses attention to learn filters directly, which makes it scalable for large-scale graphs. This method is part of the PolyFormer model, which combines PolyAttn with Graph Transformer models. The result is a model that can capture spectral information and be efficient while still being powerful. The authors tested their approach on different types of graphs and showed it works well. |
Keywords
* Artificial intelligence * Attention * Representation learning * Transformer
