Graph representation learning has become a crucial task in machine learning and data mining due to its potential for modeling complex structures such as social networks, chemical compounds, and biological systems. Spiking neural networks (SNNs) have recently emerged as a promising alternative to traditional neural networks for graph learning tasks, benefiting from their ability to efficiently encode and process temporal and spatial information. In this paper, we propose a novel approach that integrates attention mechanisms with SNNs to improve graph representation learning. Specifically, we introduce an attention mechanism for SNN that can selectively focus on important nodes and corresponding features in a graph during the learning process. We evaluate our proposed method on several benchmark datasets and show that it achieves comparable performance compared to existing graph learning techniques.

翻译：图表示学习因其在建模复杂结构（如社交网络、化合物和生物系统）中的潜力，已成为机器学习和数据挖掘中的关键任务。脉冲神经网络（SNNs）凭借其高效编码和处理时空信息的能力，近年来作为传统神经网络在图学习任务中的一种有前景的替代方案而崭露头角。在本文中，我们提出了一种将注意力机制与SNNs相结合的新方法，以改进图表示学习。具体而言，我们引入了一种针对SNN的注意力机制，该机制能够在学习过程中选择性关注图中重要节点及其对应特征。我们在多个基准数据集上对所提方法进行了评估，结果表明，与现有图学习技术相比，该方法取得了可比的性能。