Summary of Grokformer: Graph Fourier Kolmogorov-arnold Transformers, by Guoguo Ai et al.
GrokFormer: Graph Fourier Kolmogorov-Arnold Transformers
by Guoguo Ai, Guansong Pang, Hezhe Qiao, Yuan Gao, Hui Yan
First submitted to arxiv on: 26 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 Graph Fourier Kolmogorov-Arnold Transformer (GrokFormer) model, which learns highly expressive spectral filters with adaptive graph spectrum and order through a Fourier series modeling over learnable activation functions. GrokFormer is designed to tackle the limitation of Graph Transformers (GTs) in capturing high-frequency signals in graph features, building upon the self-attention mechanism. The proposed model outperforms state-of-the-art GTs and GNNs on 15 real-world node classification and graph classification datasets across various domains, scales, and graph properties. The code is available at this GitHub URL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to analyze complex networks called Graph Fourier Kolmogorov-Arnold Transformer (GrokFormer). GrokFormer helps computers better understand these networks by learning how to capture different patterns and signals within them. This approach is more effective than previous methods in capturing important information hidden in the data. The researchers tested their model on many real-world datasets and found that it outperformed other popular approaches. This breakthrough has the potential to improve our understanding of complex networks in various fields. |
Keywords
» Artificial intelligence » Classification » Self attention » Transformer
