Summary of Graph External Attention Enhanced Transformer, by Jianqing Liang and Min Chen and Jiye Liang
Graph External Attention Enhanced Transformer
by Jianqing Liang, Min Chen, Jiye Liang
First submitted to arxiv on: 31 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
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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 This paper proposes a novel attention mechanism, Graph External Attention (GEA), which leverages external node and edge key-value units to capture inter-graph correlations. The authors design an architecture called GEAET, which integrates local structure and global interaction information for more comprehensive graph representations. The GEAET model achieves state-of-the-art empirical performance on benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Transformer architecture has been applied to graph representation learning, overcoming limitations of Graph Neural Networks (GNNs). This paper proposes a new attention mechanism that captures inter-graph correlations, leading to better graph representations. The authors design an effective architecture that integrates local and global information for more comprehensive results. |
Keywords
» Artificial intelligence » Attention » Representation learning » Transformer
