Summary of Vcr-graphormer: a Mini-batch Graph Transformer Via Virtual Connections, by Dongqi Fu et al.
VCR-Graphormer: A Mini-batch Graph Transformer via Virtual Connections
by Dongqi Fu, Zhigang Hua, Yan Xie, Jin Fang, Si Zhang, Kaan Sancak, Hao Wu, Andrey Malevich, Jingrui He, Bo Long
First submitted to arxiv on: 24 Mar 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 proposed Virtual Connection Ranking based Graph Transformer (VCR-Graphormer) addresses the limitations of traditional graph transformers in handling large-scale graph data. The existing method performs dense attention, resulting in quadratic computational costs that hinder efficient training on massive datasets. To overcome this challenge, the authors introduce a novel approach called PPR tokenization, which decouples model training from complex graph topological information and makes heavy feature engineering offline and independent. This allows for mini-batch training of graph transformers by loading each node’s token list in batches. The paper also demonstrates that VCR-Graphormer is viable as a graph convolution network with a fixed polynomial filter and jumping knowledge. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Graph Transformer, a powerful tool for analyzing complex data structures like graphs, has some limitations when dealing with very large datasets. One problem is that the method uses attention, which requires a lot of computation. To solve this issue, researchers have developed a new approach called PPR tokenization. This method separates the model training from the complex graph information and allows for offline feature engineering. As a result, it’s now possible to train the Graph Transformer in small batches, making it more efficient. |
Keywords
* Artificial intelligence * Attention * Feature engineering * Token * Tokenization * Transformer
