Structure-Adaptive Threshold Learning via sparse representation for Spiking Graph Neural Network
Spiking graph neural networks (SGNNs) have attracted considerable attention due to their high efficiency and low energy consumption in processing graph-structured data. In such networks, the firing threshold of neurons serves as a critical gating mechanism that governs spike sparsity, information flow, and energy consumption. However, existing threshold mechanisms are either fixed global constants or rely solely on coarse-grained statistics such as node degree. These approaches fundamentally fail to capture the rich local structural heterogeneity inherent in graph-structured data. In such data, nodes naturally assume distinct structural roles. Consequently, the resulting spike trains retain insufficient discriminative structural information. This deficiency compromises model capability and robustness against local structural distribution shifts. To address this issue, we propose a structure-adaptive threshold learning framework based on sparse representation. The framework learns a dictionary in which each atom captures a typical local subgraph pattern and is associated with a learnable threshold. Soft weights derived from sparse coding are leveraged to aggregate the atomic thresholds via weighted averaging, yielding a node-specific firing threshold. Furthermore, we design an alternating soft-fusion-hard-grouping training strategy that decouples structure-aware threshold generation from pattern-specific threshold optimization. Experimental results on multiple benchmark semi-supervised node classification tasks demonstrate that the proposed method significantly improves classification accuracy while preserving high spike sparsity. These results validate the effectiveness of the structure-adaptive threshold mechanism for low-power spiking graph learning.