  In recent years, spiking neural networks (SNNs) have attracted substantial
interest due to their potential to replicate the energy-efficient and
event-driven processing of biological neurons. Despite this, the application of
SNNs in graph representation learning, particularly for non-Euclidean data,
remains underexplored, and the influence of spiking dynamics on graph learning
is not yet fully understood. This work seeks to address these gaps by examining
the unique properties and benefits of spiking dynamics in enhancing graph
representation learning. We propose a spike-based graph neural network model
that incorporates spiking dynamics, enhanced by a novel spatial-temporal
feature normalization (STFN) technique, to improve training efficiency and
model stability. Our detailed analysis explores the impact of rate coding and
temporal coding on SNN performance, offering new insights into their advantages
for deep graph networks and addressing challenges such as the oversmoothing
problem. Experimental results demonstrate that our SNN models can achieve
competitive performance with state-of-the-art graph neural networks (GNNs)
while considerably reducing computational costs, highlighting the potential of
SNNs for efficient neuromorphic computing applications in complex graph-based
scenarios.
