Energy-Based Single-Layer Spiking Graph Neural Networks

Spiking graph neural networks have shown strong potential for efficient graph representation learning due to their discrete communication mechanism. However, most existing methods still inherit the hierarchically stacked message-passing paradigm of conventional graph neural networks and rely on multi-step output averaging for prediction, making them vulnerable to over-smoothing, spike information degradation, and high inference latency. To address these issues, we propose an energy-based single-layer spiking graph representation learning model that formulates node representation learning as a finite-horizon spiking dynamical process on graphs. Specifically, we decouple the neuronal dynamics into three components: a constant external current for mitigating over-smoothing, a bidirectional graph attention current for structural interaction, and a Hopfield current for refining node states via prior feature memory. Furthermore, we establish a rigorous theoretical framework based on a global energy functional, mathematically proving that the network's temporal evolution intrinsically executes an energy minimization process, thereby guaranteeing system stability and convergence. Extensive node classification experiments on eight benchmark datasets demonstrate that the proposed method achieves competitive performance while exhibiting clear advantages in dynamical stability, anti-over-smoothing capability, and inference efficiency.