Summary of Ntformer: a Composite Node Tokenized Graph Transformer For Node Classification, by Jinsong Chen et al.
NTFormer: A Composite Node Tokenized Graph Transformer for Node Classification
by Jinsong Chen, Siyu Jiang, Kun He
First submitted to arxiv on: 27 Jun 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 Recently, graph Transformers have made significant advancements in node classification on graphs. A crucial step involves transforming input graphs into token sequences as model inputs, enabling effective learning of node representations. However, existing methods only express partial graph information through single-type token generation, requiring tailored strategies to encode additional graph-specific features into the Transformer. To address this issue, we propose NTFormer, a new graph Transformer that introduces Node2Par, a novel token generator constructing various token sequences using different token elements for each node. This flexibility enables comprehensive expression of rich graph features. Benefiting from Node2Par’s merits, NTFormer leverages a Transformer-based backbone without graph-specific modifications to learn node representations, eliminating the need for such modifications. Extensive experiments on benchmark datasets containing homophily and heterophily graphs with different scales demonstrate NTFormer’s superiority over representative graph Transformers and graph neural networks for node classification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Recently, researchers have made big progress in using computers to understand graph data. Graphs are like maps that show relationships between things. To do this, they use a special kind of computer program called a Transformer. The problem is that most Transformations don’t fully capture all the information in the graph. They propose a new way to do this called NTFormer. It uses a different approach to create “tokens” (like words) that represent each node in the graph. This helps the computer understand more about the relationships between things in the graph. The researchers tested NTFormer on many different types of graphs and showed that it is better than other methods at understanding these graphs. |
Keywords
* Artificial intelligence * Classification * Token * Transformer
