  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.
