动态图链路预测通过图上的历史交互预测未来节点间链路的形成或消失。为减少基于循环神经网络建模网络动态的方法在细粒度时间的动态图上具有较高能耗的问题，提出一种基于脉冲神经网络优化的动态图链路预测模型，通过融合脉冲神经网络的节点记忆更新模块，脉冲化节点记忆的更新过程，训练图神经网络学习动态图的演化动态并实现链路预测。在3个公开经典数据集上的结果表明，模型在运行速度上得到提升，并保留了准确性，在动态图链路预测任务中具有较好的性能表现。

Dynamic graph link prediction aims to predict the formation or disappearance of links between nodes in a graph based on their historical interactions. To address the issue of high energy consumption associated with modeling dynamic networks using recurrent neural networks at fine-grained temporal graphs, a dynamic graph link prediction model optimized by spiking neural networks was proposed. By the node memory updater incorporated spiking neural networks and the spiking update process of node memory, the evolving dynamics of dynamic graphs were learned by graph neural networks and the model achieved link prediction. The results on three publicly available classic datasets show that the proposed model exhibits improved runtime efficiency while maintaining accuracy, showcasing favorable performance in dynamic graph link prediction tasks.