Abstract
Heterogeneous network link prediction is an important network information mining problem. Existing link prediction methods for heterogeneous networks typically require predefined meta-paths with prior knowledge. To address the problem, we propose a new model, named Heterogeneous Line Graph Neural Network (HLGNN), in this paper. Firstly, we design a line graph transformation module to encapsulate node features and transform the heterogeneous network into a heterogeneous line graph. Then, we propose an intra-type aggregation component to collect the same type of edges. As we have aggregated node information in each type, we design an inter-layer aggregation to combine messages from multiple node types. Finally, we put the aggregation results into a multilayer perceptron to achieve link prediction. The experimental results show that, compared to the state-of-the-art baselines, the proposed method achieves superior performance.
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Acknowledgments
This study was supported in part by the Science and Technology Program of Gansu Province (Nos. 21JR7RA458 and 21ZD8RA008), and the Supercomputing Center of Lanzhou University.
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Sun, Y., Zhao, Y., Li, L., Dong, H. (2023). Heterogeneous Line Graph Neural Network for Link Prediction. In: Yang, X., et al. Advanced Data Mining and Applications. ADMA 2023. Lecture Notes in Computer Science(), vol 14180. Springer, Cham. https://doi.org/10.1007/978-3-031-46677-9_1
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DOI: https://doi.org/10.1007/978-3-031-46677-9_1
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