# GT-SNT: A Linear-Time Transformer for Large-Scale Graphs via Spiking Node Tokenization

arXiv: https://arxiv.org/abs/2504.11840 · HTML: https://mlsanity.com/paper/2504.11840/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2025.

## Built on by (most cited first, 2 of 2)

- [SGNNBench: A Holistic Evaluation of Spiking Graph Neural Network on Large-scale Graph](https://mlsanity.com/paper/2509.21342/) (2025)
- [Scalable and Efficient Joint Spiking Embedding Predictive Architecture for Large-Scale Dynamic Graphs](https://mlsanity.com/paper/2607.18412/) (2026)

## Abstract (arXiv)

Graph Transformers (GTs), which integrate message passing and self-attention mechanisms simultaneously, have achieved promising empirical results in graph prediction tasks. However, the design of scalable and topology-aware node tokenization has lagged behind other modalities. This gap becomes critical as the quadratic complexity of full attention renders them impractical on large-scale graphs. Recently, Spiking Neural Networks (SNNs), as brain-inspired models, provided an energy-saving scheme to convert input intensity into discrete spike-based representations through event-driven spiking neurons. Inspired by these characteristics, we propose a linear-time Graph Transformer with Spiking Node Tokenization (GT-SNT) for node classification. By integrating multi-step feature propagation with SNNs, spiking node tokenization generates compact, locality-aware spike count embeddings as node tokens to avoid predefined codebooks and their utilization issues. The codebook guided self-attention leverages these tokens to perform node-to-token attention for linear-time global context aggregation. In experiments, we compare GT-SNT with other state-of-the-art baselines on node classification datasets ranging from small to large. Experimental results show that GT-SNT achieves comparable performances on most datasets and reaches up to 130x faster inference speed compared to other GTs.