Summary of Learning a Mini-batch Graph Transformer Via Two-stage Interaction Augmentation, by Wenda Li et al.
Learning a Mini-batch Graph Transformer via Two-stage Interaction Augmentation
by Wenda Li, Kaixuan Chen, Shunyu Liu, Tongya Zheng, Wenjie Huang, Mingli Song
First submitted to arxiv on: 13 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 Mini-batch Graph Transformer (MGT) model has shown promise in semi-supervised node prediction tasks, offering improved computational efficiency and robustness. However, existing methods for processing local information have limitations, such as relying on sampling or simple aggregation, which can result in the loss of critical neighbor information. To address this, researchers propose LGMformer, a novel MGT model that employs a two-stage augmented interaction strategy to transition from local to global perspectives. This approach uses a neighbor-target interaction Transformer (NTIformer) to understand co-interaction patterns and a cross-attention mechanism for incorporating entire graph prototypes into the target node representation. The proposed method, LGMformer, achieves enhanced node representations under MGT, demonstrating effectiveness in node classification on ten benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to learn from graphs, which are collections of nodes connected by edges. The authors want to improve the accuracy and efficiency of this process. They propose a new model called LGMformer that can capture both local and global patterns in the graph. This is important because ignoring either the local or global information can result in poor performance. The authors tested their model on ten different datasets and showed that it outperforms other methods. |
Keywords
* Artificial intelligence * Classification * Cross attention * Semi supervised * Transformer
