Networks are one of the most valuable data structures for modeling problems in the real world. However, the most recent node embedding strategies have focused on undirected graphs, with limited attention to directed graphs, especially directed heterogeneous graphs. In this study, we first investigated the network properties of directed heterogeneous graphs. Based on network analysis, we proposed an embedding method, a bidirectional heterogeneous graph neural network with random teleport (BHGNN-RT), for directed heterogeneous graphs, that leverages bidirectional message-passing process and network heterogeneity. With the optimization of teleport proportion, BHGNN-RT is beneficial to overcome the over-smoothing problem. Extensive experiments on various datasets were conducted to verify the efficacy and efficiency of BHGNN-RT. Furthermore, we investigated the effects of message components, model layer, and teleport proportion on model performance. The performance comparison with all other baselines illustrates that BHGNN-RT achieves state-of-the-art performance, outperforming the benchmark methods in both node classification and unsupervised clustering tasks.

翻译：网络是建模现实世界问题最有价值的数据结构之一。然而，最新的节点嵌入策略主要关注无向图，对于有向图尤其是异质有向图的关注有限。本研究首先探讨了有向异质图的网络特性，基于网络分析提出了一种适用于有向异质图的嵌入方法——具备随机传送能力的双向异质图神经网络（BHGNN-RT）。该方法利用双向消息传递过程与网络异质性，并通过优化传送比例有助于克服过平滑问题。我们在不同数据集上进行了广泛实验以验证BHGNN-RT的效能与效率，进一步探究了消息组件、模型层数与传送比例对模型性能的影响。与所有其他基线方法的性能对比表明，BHGNN-RT在节点分类与无监督聚类任务中均实现了最先进的性能，超越了基准方法。