mlsanityData to 7 Oct 2026

BHGNN-RT: Network embedding for directed heterogeneous graphs

arXiv 2311.14404 ↗First posted 24 Nov 2023cs.LGcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 27 of its 46 references that are in the map. First posted on arXiv 24 Nov 2023.

Builds on

27 of 46 references.

References · 27

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15 more
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