Real-world graphs or networks are usually heterogeneous, involving multiple types of nodes and relationships. Heterogeneous graph neural networks (HGNNs) can effectively handle these diverse nodes and edges, capturing heterogeneous information within the graph, thus exhibiting outstanding performance. However, most methods of HGNNs usually involve complex structural designs, leading to problems such as high memory usage, long inference time, and extensive consumption of computing resources. These limitations pose certain challenges for the practical application of HGNNs, especially for resource-constrained devices. To mitigate this issue, we propose the Spiking Heterogeneous Graph Attention Networks (SpikingHAN), which incorporates the brain-inspired and energy-saving properties of Spiking Neural Networks (SNNs) into heterogeneous graph learning to reduce the computing cost without compromising the performance. Specifically, SpikingHAN aggregates metapath-based neighbor information using a single-layer graph convolution with shared parameters. It then employs a semantic-level attention mechanism to capture the importance of different meta-paths and performs semantic aggregation. Finally, it encodes the heterogeneous information into a spike sequence through SNNs, simulating bioinformatic processing to derive a binarized 1-bit representation of the heterogeneous graph. Comprehensive experimental results from three real-world heterogeneous graph datasets show that SpikingHAN delivers competitive node classification performance. It achieves this with fewer parameters, quicker inference, reduced memory usage, and lower energy consumption. Code is available at https://github.com/QianPeng369/SpikingHAN.

翻译：现实世界中的图或网络通常是异构的，涉及多种类型的节点和关系。异构图神经网络（HGNNs）能够有效处理这些多样化的节点和边，捕捉图中的异构信息，从而展现出卓越的性能。然而，大多数HGNN方法通常涉及复杂的结构设计，导致高内存占用、长推理时间以及大量计算资源消耗等问题。这些限制为HGNN的实际应用带来了一定挑战，特别是对于资源受限的设备。为缓解这一问题，我们提出脉冲异构图注意力网络（SpikingHAN），它将脉冲神经网络（SNNs）的仿脑与节能特性融入异构图学习，在不损失性能的前提下降低计算成本。具体而言，SpikingHAN通过具有共享参数的单层图卷积聚合基于元路径的邻居信息，随后采用语义级注意力机制捕获不同元路径的重要性并进行语义聚合。最后，通过SNNs将异构信息编码为脉冲序列，模拟生物信息处理过程，从而得到异构图的二值化1比特表示。在三个真实世界异构图数据集上的综合实验结果表明，SpikingHAN在节点分类任务上具有竞争力的性能，同时实现了更少的参数量、更快的推理速度、更低的内存占用和能耗。代码公开于https://github.com/QianPeng369/SpikingHAN。