A heterogeneous graph consists of different vertices and edges types. Learning on heterogeneous graphs typically employs meta-paths to deal with the heterogeneity by reducing the graph to a homogeneous network, guide random walks or capture semantics. These methods are however sensitive to the choice of meta-paths, with suboptimal paths leading to poor performance. In this paper, we propose an approach for learning on heterogeneous graphs without using meta-paths. Specifically, we decompose a heterogeneous graph into different homogeneous relation-type graphs, which are then combined to create higher-order relation-type representations. These representations preserve the heterogeneity of edges and retain their edge directions while capturing the interaction of different vertex types multiple hops apart. This is then complemented with attention mechanisms to distinguish the importance of the relation-type based neighbors and the relation-types themselves. Experiments demonstrate that our model generally outperforms other state-of-the-art baselines in the vertex classification task on three commonly studied heterogeneous graph datasets.

翻译：异构图由不同类型的顶点和边组成。异构图的通常学习方法利用元路径来降低异构性，将图简化为同构网络、引导随机游走或捕获语义。然而，这些方法对元路径的选择较为敏感，次优路径会导致性能下降。本文提出一种无需元路径的异构图学习方法。具体而言，我们将异构图分解为不同的同构关系类型图，再将这些图组合以创建更高阶的关系类型表示。这些表示在保留边异质性及方向的同时，能捕获相隔多跳的不同顶点类型间的交互。在此基础上，我们引入注意力机制以区分基于关系类型的邻居及关系类型本身的重要性。实验表明，在三个常用异构图数据集的顶点分类任务中，我们的模型普遍优于其他现有基线方法。