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McH-HGCN: multi-curvature hyperbolic heterogeneous graph convolutional network with type triplets

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Abstract

Most existing representation learning models for heterogeneous graphs depend on meta-paths, which requires domain-specific prior knowledge and reduces model practicality. In addition, real-world graphs usually conform to power-law distributions, and conventional graph models defined in Euclidean space lead to high distortion for such data. In this paper, we propose a Multi-curvature Hyperbolic Heterogeneous Graph Convolutional Network (McH-HGCN) based on the graph’s inherent <source node, relation, target node> type triplets. By selecting triplets as data units for message passing and defining the model in hyperbolic space, our model caters to the power-law properties of heterogeneous graphs while avoiding meta-paths dependence. To model the heterogeneity of the graph, we set distinct hyperbolic curvatures as learnable parameters for different types of nodes to obtain the optimal parameterized space mapping after training. Additionally, we introduce a dynamic heterogeneous attention mechanism to compute the attention weights for heterogeneous neighbor aggregation. Node classification and recommendation experiments with several heterogeneous graph datasets show that our model outperforms state-of-the-art methods on multiple datasets, achieving excellent performance without relying on meta-paths.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Notes

  1. https://github.com/Andy-Border/NSHE.

  2. https://github.com/acbull/pyHGT.

  3. https://github.com/dmlc/dgl.

  4. https://github.com/optuna/optuna

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Funding

The Funding was provided by State Key Laboratory of Software Development Environment (Grant No. SKLSDE-2020ZX-02).

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  1. State Key Laboratory of Software Development Environment, Beihang University, XueYuan Road, Beijing, 100191, China

    Yanxi Liu & Bo Lang

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Liu, Y., Lang, B. McH-HGCN: multi-curvature hyperbolic heterogeneous graph convolutional network with type triplets. Neural Comput & Applic 35, 15033–15049 (2023). https://doi.org/10.1007/s00521-023-08473-5

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