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An interlayer feature fusion-based heterogeneous graph neural network

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Abstract

Most existing heterogeneous graph neural network models need more effective integration and full exploitation of features at different network levels to prevent overfitting. To address these problems, we propose an interlayer feature fusion-based heterogeneous graph neural network model, FHGN. To enhance the representation of the original input features, a residual graph attention layer is presented to aggregate neighborhood node messages and updates the central node representation. To improve the perception ability of different nodes for neighborhood information, an interlayer feature fusion layer with different strategies is designed to integrate low-order embedding and high-order semantic information adaptively. To reduce the homogeneity for node embedding, a semantic feature fusion layer is proposed to fuse the node embeddings covering semantic information learned from the different meta-path subgraphs. Experiments on several datasets show that our model outperforms the existing related works.

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Acknowledgements

We would like to thank Professor Xin Wang and Zhiyong Feng from the College of Intelligence and Computing of Tianjin University for their valuable revising of the manuscript

Funding

This research was partially funded by the National Natural Science Foundation of China (NSFC), No. 61832014 and 61373165

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Authors and Affiliations

  1. College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China

    Ke Feng, Guozheng Rao & Qing Cong

  2. School of Economics and Management, Tianjin University of Science and Technology, Tianjin, 300357, China

    Li Zhang

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  1. Ke Feng
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  2. Guozheng Rao
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  3. Li Zhang
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  4. Qing Cong
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Contributions

Conceptualization: Guozheng Rao, Ke Feng, Qing Cong, Li Zhang; Methodology: Guozheng Rao, Qing Cong, Ke Feng; Formal analysis and investigation: Qing Cong, Ke Feng; Writing - original draft preparation: Ke Feng, Guozheng Rao, Qing Cong, Li Zhang; Writing - review and editing: Li Zhang; Funding acquisition: Guozheng Rao; Resources: Qing Cong; Supervision: Guozheng Rao, Li Zhang

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Correspondence to Guozheng Rao or Li Zhang.

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Guozheng Rao, Li Zhang and Qing Cong these authors contributed equally to this work.

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Feng, K., Rao, G., Zhang, L. et al. An interlayer feature fusion-based heterogeneous graph neural network. Appl Intell 53, 25626–25639 (2023). https://doi.org/10.1007/s10489-023-04840-w

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