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Graph-Agnostic Linear Transformers.

Zhiyu Guo1, Yang Liu2, Xiang Ao3

  • 1organization=State Key Lab of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences, city=Beijing, postcode=100190, country=China; organization=University of Chinese Academy of Sciences, city=Beijing, postcode=100190, country=China.

Neural Networks : the Official Journal of the International Neural Network Society
|January 23, 2026
PubMed
Summary
This summary is machine-generated.

Graph-Agnostic Linear Transformer (GALiT) reduces computational costs by decoupling graph structures from Transformers. This efficient model outperforms existing methods on benchmark graphs.

Keywords:
Graph neural networkGraph transformerGraph-agnostic modelLinear attention

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Graph Representation Learning

Background:

  • Graph Transformers (GTs) integrate local structures and global attention for graph data.
  • However, GTs face computational challenges on large graphs due to complex attention mechanisms coupled with graph structures.

Purpose of the Study:

  • To propose a computationally efficient and graph-agnostic model for graph-structured data.
  • To reduce the computational overhead of Graph Transformers while maintaining or improving performance.

Main Methods:

  • Introduced the Graph-Agnostic Linear Transformer (GALiT) by decoupling graph structures from Transformers.
  • Utilized graph structures solely for denoising node features before training.
  • Simplified linear attention mechanisms and integrated denoised features via weighted combination.

Main Results:

  • GALiT significantly reduces computational complexity by excluding graph structures during training and inference.
  • The model achieves high efficiency while maintaining or enhancing performance compared to GNNs and GTs.
  • Experimental results on benchmark graphs validate the effectiveness of GALiT.

Conclusions:

  • GALiT offers a computationally efficient and effective alternative to existing Graph Transformers.
  • The proposed method demonstrates the potential of graph-agnostic approaches in representation learning.
  • GALiT successfully balances efficiency and performance in graph-structured data analysis.