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HGNN-ETA: Heterogeneous graph neural network enriched with text attribute

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Abstract

Heterogeneous graphs can accurately and effectively model rich semantic information and complex network relationships in the real world. As a deep representation model for nodes, heterogeneous graph neural networks (HGNNs) offer powerful graph data processing capabilities and exhibit outstanding performance in network analysis tasks. However, the traditional HGNNs model cannot mine all the semantic text information during text attribute vector learning; this results in a loss of node information, which degrades the model performance. In this study, we propose a novel framework for the HGNN model enriched with text attribute (HGNN-ETA), which incorporates the text attributes of the nodes. This process involves the attention mechanism-based encoding of the node text attributes, neighborhood aggregation mechanism-based completion of the node attributes, and end-to-end construction and optimization of a heterogeneous graph model. Extensive experiments and comparisons were conducted on two real-world heterogeneous graph-structured datasets. The results demonstrate that the proposed framework is more effective than other state-of-the-art models.

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Notes

  1. https://dblp.uni-trier.de

  2. https://www.imdb.com/

  3. https://github.com/wz1714748313/HGNN-ETA

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Acknowledgements

This research is supported by the National Natural Science Foundation of China (Grant No. 62072288, 61702306), the Taishan Scholar Program of Shandong Province, the Natural Science Foundation of Shandong Province (Grant No. ZR2022MF268).

Funding

This study was funded by the National Natural Science Foundation of China (Grant No. 62072288, 61702306), the Taishan Scholar Program of Shandong Province, the Natural Science Foundation of Shandong Province (Grant No. ZR2022MF268).

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Authors and Affiliations

  1. College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao, 266590, Shandong, China

    Chao Li, Zhen Wang & Qingtian Zeng

  2. College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590, Shandong, China

    Zhongying Zhao, Hua Duan & Qingtian Zeng

Authors
  1. Chao Li
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  2. Zhen Wang
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  3. Zhongying Zhao
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  4. Hua Duan
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  5. Qingtian Zeng
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Contributions

Chao Li, Zhen Wang, Zhongying Zhao wrote the main manuscript text; Zhen Wang prepared the result of our experiments; All authors reviewed the manuscript.

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Correspondence to Chao Li.

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Li, C., Wang, Z., Zhao, Z. et al. HGNN-ETA: Heterogeneous graph neural network enriched with text attribute. World Wide Web 26, 1913–1934 (2023). https://doi.org/10.1007/s11280-022-01120-4

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