Heterogeneous graph neural networks (HGNNs) have powerful capability to embed rich structural and semantic information of a heterogeneous graph into node representations. Existing HGNNs inherit many mechanisms from graph neural networks (GNNs) over homogeneous graphs, especially the attention mechanism and the multi-layer structure. These mechanisms bring excessive complexity, but seldom work studies whether they are really effective on heterogeneous graphs. This paper conducts an in-depth and detailed study of these mechanisms and proposes Simple and Efficient Heterogeneous Graph Neural Network (SeHGNN). To easily capture structural information, SeHGNN pre-computes the neighbor aggregation using a light-weight mean aggregator, which reduces complexity by removing overused neighbor attention and avoiding repeated neighbor aggregation in every training epoch. To better utilize semantic information, SeHGNN adopts the single-layer structure with long metapaths to extend the receptive field, as well as a transformer-based semantic fusion module to fuse features from different metapaths. As a result, SeHGNN exhibits the characteristics of simple network structure, high prediction accuracy, and fast training speed. Extensive experiments on five real-world heterogeneous graphs demonstrate the superiority of SeHGNN over the state-of-the-arts on both accuracy and training speed.

翻译：异构图神经网络（HGNNs）具备将异构图中丰富的结构和语义信息嵌入到节点表示中的强大能力。现有HGNNs继承了许多同构图上的图神经网络（GNNs）机制，尤其是注意力机制和多层结构。这些机制带来了过高的复杂度，但很少有研究探讨它们在异构图上是否真正有效。本文对这些机制进行了深入细致的研究，并提出了简单高效的异构图神经网络（SeHGNN）。为了轻松捕获结构信息，SeHGNN使用轻量级均值聚合器预计算邻居聚合，通过移除过度使用的邻居注意力并避免每个训练轮次重复进行邻居聚合来降低复杂度。为了更好地利用语义信息，SeHGNN采用带有长元路径的单层结构以扩展感受野，并基于Transformer的语义融合模块来融合不同元路径的特征。因此，SeHGNN展现出网络结构简单、预测精度高和训练速度快的特性。在五个真实世界异构图上的大量实验表明，SeHGNN在准确性和训练速度上均优于当前最先进方法。