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Link Prediction for Temporal Heterogeneous Networks Based on the Information Lifecycle

Author

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  • Jiaping Cao

    (College of Systems Engineering, National University of Defense Technology, Changsha 410073, China)

  • Jichao Li

    (College of Systems Engineering, National University of Defense Technology, Changsha 410073, China)

  • Jiang Jiang

    (College of Systems Engineering, National University of Defense Technology, Changsha 410073, China)

Abstract

Link prediction for temporal heterogeneous networks is an important task in the field of network science, and it has a wide range of real-world applications. Traditional link prediction methods are mainly based on static homogeneous networks, which do not distinguish between different types of nodes in the real world and do not account for network structure evolution over time. To address these issues, in this paper, we study the link prediction problem in temporal heterogeneous networks and propose a link prediction method for temporal heterogeneous networks (LP-THN) based on the information lifecycle, which is an end-to-end encoder–decoder structure. The information lifecycle accounts for the active, decay and stable states of edges. Specifically, we first introduce the meta-path augmented residual information matrix to preserve the structure evolution mechanism and semantics in HINs, using it as input to the encoder to obtain a low-dimensional embedding representation of the nodes. Finally, the link prediction problem is considered a binary classification problem, and the decoder is utilized for link prediction. Our prediction process accounts for both network structure and semantic changes using meta-path augmented residual information matrix perturbations. Our experiments demonstrate that LP-THN outperforms other baselines in both prediction effectiveness and prediction efficiency.

Suggested Citation

  • Jiaping Cao & Jichao Li & Jiang Jiang, 2023. "Link Prediction for Temporal Heterogeneous Networks Based on the Information Lifecycle," Mathematics, MDPI, vol. 11(16), pages 1-17, August.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:16:p:3541-:d:1218602
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    References listed on IDEAS

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    1. Leo Katz, 1953. "A new status index derived from sociometric analysis," Psychometrika, Springer;The Psychometric Society, vol. 18(1), pages 39-43, March.
    2. Tao Zhou & Linyuan Lü & Yi-Cheng Zhang, 2009. "Predicting missing links via local information," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 71(4), pages 623-630, October.
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    Cited by:

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