Heterogeneous graph neural networks (HGNNs) have recently shown impressive capability in modeling heterogeneous graphs that are ubiquitous in real-world applications. Due to the diversity of attributes of nodes in different types, most existing models first align nodes by mapping them into the same low-dimensional space. However, in this way, they lose the type information of nodes. In addition, most of them only consider the interactions between nodes while neglecting the high-order information behind the latent interactions among different node features. To address these problems, in this paper, we propose a novel heterogeneous graph model MULAN, including two major components, i.e., a type-aware encoder and a dimension-aware encoder. Specifically, the type-aware encoder compensates for the loss of node type information and better leverages graph heterogeneity in learning node representations. Built upon transformer architecture, the dimension-aware encoder is capable of capturing the latent interactions among the diverse node features. With these components, the information of graph heterogeneity, node features and graph structure can be comprehensively encoded in node representations. We conduct extensive experiments on six heterogeneous benchmark datasets, which demonstrates the superiority of MULAN over other state-of-the-art competitors and also shows that MULAN is efficient.

翻译：异质图神经网络（HGNNs）近年来在建模现实应用中普遍存在的异质图方面展现出令人瞩目的能力。由于不同类型节点属性的多样性，现有模型通常首先通过将节点映射到同一低维空间来对齐节点。然而，这种方式丢失了节点的类型信息。此外，大多数模型仅考虑节点间的交互，而忽略了不同节点特征背后潜在交互中的高阶信息。为解决这些问题，本文提出了一种新型异质图模型MULAN，包含两个主要组成部分，即类型感知编码器和维度感知编码器。具体而言，类型感知编码器补偿了节点类型信息的损失，并更好地利用图异质性来学习节点表示。构建于Transformer架构之上的维度感知编码器能够捕获不同节点特征之间的潜在交互。借助这些组成部分，图异质性、节点特征和图结构信息可以被全面编码到节点表示中。我们在六个异质基准数据集上进行了广泛实验，结果表明MULAN优于其他最先进的竞争方法，同时证明了其高效性。