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GrassNet: State space model meets graph neural network
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DOI：10.1016/j.patcog.2026.113197.png)
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摘要
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• New view of graph spectral filters: Reveal limits and propose SSM-based graph filter. • SSMs for graph data: Extend SSMs to graphs via spectrum sequence modeling. • New GNN design: Propose GrassNet, integrating SSMs into spectral GNNs. • Strong results: GrassNet excels on nine benchmarks with theory and efficiency.
Keyword:
Graph spectral filters
State space models
Graph neural networks
Spectral GNNs
GrassNet
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
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引用论文
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