E2SGNN: Reconciling Expression and Efficiency in Spiking Graph Neural Network

By mimicking the brain's efficient spiking encoding paradigm, spiking graph neural networks exhibit significant potential for efficient graph data analysis. Due to the inherent expressive limitations of binary spiking signals adopted in spiking encoding, existing models typically enhance their expression by integrating numerous real-valued multiplication-additions or high-latency encoding. However, such integrations compromise the core efficiency superiority of spiking models, limiting their scalability in real-world applications. To simultaneously reconcile considerable expression and efficiency, we propose E2SGNN, a novel network comprising a dual-scale modulated spiking backbone and a latency-dynamic optimization module. The former backbone integrates global and local real-valued graph modulations into spiking graph convolution, enabling discriminative dual-scale neighbor embedding in the encoding process. It both breaks through binary spiking signals' expressive limitations and improves the content expressiveness of spiking graph representations, while retaining low-latency and addition-only efficient advantages. Moreover, to further reduce the latency redundancy for higher efficiency, the latter module adaptively customizes the latency for each graph data based on data complexity. In this way, our network can finally generate graph representations expressively and efficiently. Experiments on various datasets demonstrate the superiority of our network in expression and efficiency.