IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i16p3541-d1218602.html
   My bibliography  Save this article

Link Prediction for Temporal Heterogeneous Networks Based on the Information Lifecycle

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/16/3541/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/11/16/3541/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Leo Katz, 1953. "A new status index derived from sociometric analysis," Psychometrika, Springer;The Psychometric Society, vol. 18(1), pages 39-43, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Guoqiang Sun & Xiaoyan Qi & Qiang Zhao & Wei Wang & Yujun Li, 2024. "SVSeq2Seq: An Efficient Computational Method for State Vectors in Sequence-to-Sequence Architecture Forecasting," Mathematics, MDPI, vol. 12(2), pages 1-17, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Karimi, Fatemeh & Lotfi, Shahriar & Izadkhah, Habib, 2021. "Community-guided link prediction in multiplex networks," Journal of Informetrics, Elsevier, vol. 15(4).
    2. Lin, Dan & Wu, Jiajing & Xuan, Qi & Tse, Chi K., 2022. "Ethereum transaction tracking: Inferring evolution of transaction networks via link prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    3. Shenshen Bai & Longjie Li & Jianjun Cheng & Shijin Xu & Xiaoyun Chen, 2018. "Predicting Missing Links Based on a New Triangle Structure," Complexity, Hindawi, vol. 2018, pages 1-11, December.
    4. Wang, Zuxi & Wu, Yao & Li, Qingguang & Jin, Fengdong & Xiong, Wei, 2016. "Link prediction based on hyperbolic mapping with community structure for complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 609-623.
    5. Lee, Yan-Li & Zhou, Tao, 2021. "Collaborative filtering approach to link prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).
    6. Moradabadi, Behnaz & Meybodi, Mohammad Reza, 2016. "Link prediction based on temporal similarity metrics using continuous action set learning automata," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 460(C), pages 361-373.
    7. Wu, Tao & Chen, Leiting & Zhong, Linfeng & Xian, Xingping, 2017. "Predicting the evolution of complex networks via similarity dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 662-672.
    8. Zhang, Xue & Wang, Xiaojie & Zhao, Chengli & Yi, Dongyun & Xie, Zheng, 2014. "Degree-corrected stochastic block models and reliability in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 393(C), pages 553-559.
    9. Kai Yang & Yuan Liu & Zijuan Zhao & Xingxing Zhou & Peijin Ding, 2023. "Graph attention network via node similarity for link prediction," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 96(3), pages 1-10, March.
    10. Zhou, Yinzuo & Wu, Chencheng & Tan, Lulu, 2021. "Biased random walk with restart for link prediction with graph embedding method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 570(C).
    11. Mungo, Luca & Lafond, François & Astudillo-Estévez, Pablo & Farmer, J. Doyne, 2023. "Reconstructing production networks using machine learning," Journal of Economic Dynamics and Control, Elsevier, vol. 148(C).
    12. Ziqi Gao & Chenran Jiang & Jiawen Zhang & Xiaosen Jiang & Lanqing Li & Peilin Zhao & Huanming Yang & Yong Huang & Jia Li, 2023. "Hierarchical graph learning for protein–protein interaction," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    13. Lü, Linyuan & Zhou, Tao, 2011. "Link prediction in complex networks: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(6), pages 1150-1170.
    14. Zhou, Tao & Lee, Yan-Li & Wang, Guannan, 2021. "Experimental analyses on 2-hop-based and 3-hop-based link prediction algorithms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 564(C).
    15. Zeng, Shan, 2016. "Link prediction based on local information considering preferential attachment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 443(C), pages 537-542.
    16. Yu, Jiating & Wu, Ling-Yun, 2022. "Multiple Order Local Information model for link prediction in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    17. Wang, Xiaojie & Zhang, Xue & Zhao, Chengli & Xie, Zheng & Zhang, Shengjun & Yi, Dongyun, 2015. "Predicting link directions using local directed path," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 260-267.
    18. Wenjun Li & Ting Li & Kamal Berahmand, 2023. "An effective link prediction method in multiplex social networks using local random walk towards dependable pathways," Journal of Combinatorial Optimization, Springer, vol. 45(1), pages 1-27, January.
    19. Charikhi, Mourad, 2024. "Association of the PageRank algorithm with similarity-based methods for link prediction in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).
    20. Ankita Singh & Nanhay Singh, 2022. "An approach for predicting missing links in social network using node attribute and path information," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(2), pages 944-956, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:16:p:3541-:d:1218602. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.