BHGNN-RT: Network embedding for directed heterogeneous graphs
This addresses a gap in network embedding for directed heterogeneous graphs, which is important for modeling real-world problems, but it appears incremental as it builds on existing graph neural network approaches.
The paper tackled the problem of node embedding for directed heterogeneous graphs, which had received limited attention, and proposed BHGNN-RT, a method that achieved state-of-the-art performance in node classification and clustering tasks.
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.