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cs.NE2025
SGNNBench: A Holistic Evaluation of Spiking Graph Neural Network on Large-scale Graph
Huizhe Zhang, Jintang Li, Yuchang Zhu +2
Graph Neural Networks (GNNs) are exemplary deep models designed for graph data. Message passing mechanism enables GNNs to effectively capture graph topology and push the performanc…
cs.NE2025
GT-SNT: A Linear-Time Transformer for Large-Scale Graphs via Spiking Node Tokenization
Huizhe Zhang, Jintang Li, Yuchang Zhu +2
Graph Transformers (GTs), which integrate message passing and self-attention mechanisms simultaneously, have achieved promising empirical results in graph prediction tasks. However…
cs.NE2024
SGHormer: An Energy-Saving Graph Transformer Driven by Spikes
Huizhe Zhang, Jintang Li, Liang Chen +1
Graph Transformers (GTs) with powerful representation learning ability make a huge success in wide range of graph tasks. However, the costs behind outstanding performances of GTs a…