A Spiking Framework for Efficient Graph Representation Learning

Integrating spiking neural networks (SNNs) into graph neural networks (GNNs) offers a promising approach to energy-efficient graph representation learning. However, existing integration methods can still exhibit performance gaps compared with strong real-valued GNNs, while adapting SNNs to these diverse GNN architectures remains challenging. To bridge this gap, we propose a spiking GNN framework applicable to various message-passing neural networks (MPNNs) and graph Transformers. Specifically, for MPNNs, we integrate spiking neurons between layers and reposition the linear transformation modules to the start of each layer. This structure enables the computation of linear transformation to become sparse addition operations. Moreover, our method exhibits a linear decay rate of Dirichlet energy, effectively alleviating the over-smoothing problem. For graph Transformers, we design a novel spike-based self-attention mechanism and use layer normalization to replace the softmax function, which can better align with the discrete characteristics of SNNs and greatly reduce computational overhead. We extend our method to six MPNN models and three graph Transformer models. Without sacrificing model performance, the energy consumption of spike-based models is approximately 10% of that of non-spiking counterparts.