@article{HAO2025, 
author = {Yunzhi HAO and Tongya ZHENG and Xingen WANG and Xinyu WANG and Mingli SONG and Chun CHEN and Chunyan ZHOU},
title = {Multi-view heterogeneous graph embedding method with hierarchical projection},
year = {2025},
journal = {Journal of National University of Defense Technology},
volume = {47},
number = {3},
pages = {1-9},
keywords = {mutual information, graph convolutional, heterogeneous graph embedding, multi-view heterogeneous graphs},
url = {https://www.sciopen.com/article/10.11887/j.cn.202503001},
doi = {10.11887/j.cn.202503001},
abstract = {A self-supervised graph embedding approach based on hierarchical projection network called MeghenNet(multi-view heterogeneous graph projection network) was introduced to learn low-dimensional representations from multiple views. The concept of multiple-view heterogeneous graphs was defined to explicitly allow the model to simultaneously collect information from multiple data sources for modeling heterogeneous graphs. A hierarchical attention projection that involves a cross-relation projection to extract semantics information within each view was employed, followed by a cross-view projection to aggregate contextual information from other views. The mutual information loss function between each view embedding and the global embedding was computed to ensure the information consistency across views. Experimental results on several real-world datasets demonstrate that the proposed method outperforms state-of-the-art approaches when handling multi-view heterogeneous graphs.}
}