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Link prediction for heterogeneous information networks based on enhanced meta-path aggregation and attention mechanism

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Abstract

Heterogeneous link prediction aims to reveal potential connections between two nodes in heterogeneous information networks. Most existing studies are based on meta-paths, but ignore the information contained in incomplete meta-paths. They simply aggregate meta-paths, leading to mining semantic information insufficiently. To solve this problem, we propose a link prediction model based on enhanced meta-path aggregation and attention mechanism. In this model, the deficiency of missing topological information from incomplete meta-paths is compensated by aggregating structural features and semantics. Different from existing meta-path encoders, we use recurrent neural networks and the attention mechanism to learn explicit and implicit semantic knowledge from meta-paths, which can capture more complex semantic associations between nodes. In addition, to avoid duplicate feature acquisition by random walking, we design a novel bidirectional biased random walking algorithm. It is applied to guide the generation of heterogeneous neighbors of each node that contain features ignored by the meta-path-wise model, which can mine complete topological information and get more accurate link prediction results. The extensive experiments on several datasets demonstrate that the proposed model outperforms baselines.

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Some or all data, models, or codes generated or used during the study are available from the corresponding author by request. They are also available in a repository or online.

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Funding

National Science Foundation of China, 61602491, Lunwen Wang.

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Authors and Affiliations

  1. College of Electronic Engineering, National University of Defense Technology, Hefei, 230031, China

    Hao Shao, Lunwen Wang & Rangang Zhu

Authors
  1. Hao Shao
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  2. Lunwen Wang
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  3. Rangang Zhu
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Correspondence to Lunwen Wang.

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Shao, H., Wang, L. & Zhu, R. Link prediction for heterogeneous information networks based on enhanced meta-path aggregation and attention mechanism. Int. J. Mach. Learn. & Cyber. 14, 3087–3103 (2023). https://doi.org/10.1007/s13042-023-01822-9

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