Graph neural network, as a deep learning based graph representation technology, can capture the structural information encapsulated in graphs well and generate more effective feature embedding. We have recently witnessed an emerging research interests on it. However, existing models are primarily focused on handling homogeneous graphs. When designing graph neural networks for heterogeneous graphs, heterogeneity and rich semantic information bring great challenges. In this paper, we extend graph neural network to heterogeneous graph scenes, and propose a novel high-order Symmetric Relation based Heterogeneous Graph Attention Network, denoted as SR-HGAT, which takes into account the features of nodes and high-order relations simultaneously, and exploits the two-layer attention mechanism based aggregator to efficiently capture essential semantics in an end-to-end manner. The proposed SR-HGAT first identifies the latent semantics underneath the observed explicit symmetric relations guided by different meta-paths and meta-graphs in a heterogeneous graph. The nested propagation mechanism for aggregating semantic and structural features that different links contain is then designed to calculate the interaction strength of each symmetric relation. As the core of the proposed model, to comprehensively capture both the structural and semantic feature information, a two-layer attention mechanism is applied to learn the importance of different neighborhood information as well as the weights of different symmetric relations. These latent semantics are then automatically fused to obtain unified embeddings for specific mining tasks. Extensive experimental results offer insights into the efficacy of the proposed model and have demonstrated that it significantly outperforms state-of-the-art baselines across three benchmark datasets on various downstream tasks.

中文翻译：

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SR-HGAT：基于对称关系的异构图注意力网络

图神经网络作为一种基于深度学习的图表示技术，可以很好地捕捉封装在图中的结构信息，生成更有效的特征嵌入。我们最近目睹了关于它的新兴研究兴趣。然而，现有的模型主要专注于处理同构图。在为异构图设计图神经网络时，异构性和丰富的语义信息带来了巨大的挑战。在本文中，我们将图神经网络扩展到异构图场景，并提出了一种新的基于高阶对称关系的异构图注意力网络，记为 SR-HGAT，它同时考虑了节点和高阶关系的特征，并利用基于两层注意力机制的聚合器以端到端的方式有效地捕获基本语义。提出的 SR-HGAT 首先识别在异构图中由不同元路径和元图引导的观察到的显式对称关系下的潜在语义。然后设计用于聚合​​不同链接包含的语义和结构特征的嵌套传播机制来计算每个对称关系的交互强度。作为所提出模型的核心，为了全面捕捉结构和语义特征信息，应用两层注意力机制来学习不同邻域信息的重要性以及不同对称关系的权重。这些潜在语义然后被自动融合以获得特定挖掘任务的统一嵌入。广泛的实验结果提供了对所提出模型的功效的见解，并证明它在各种下游任务的三个基准数据集上显着优于最先进的基线。