IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v40y2025i8d10.1007_s00180-025-01626-1.html

Network embedding for bipartite networks with applications in interlocking directorates in Chinese companies

Author

Listed:
  • Yan Zhang

    (Shanghai University of International Business and Economics)

  • Rui Pan

    (Central University of Finance and Economics)

  • Feifei Wang

    (Renmin University of China)

  • Kuangnan Fang

    (Xiamen University)

  • Hansheng Wang

    (Peking University)

Abstract

Bipartite networks containing two types of nodes are commonly encountered in practice. To analyze bipartite networks, network embedding models are popularly used. With the increase in data availability, nodes in networks are often observed with nodal attributes, which provide fertile information for understanding the network structure. However, existing network embedding models for dynamic bipartite networks often ignore nodal variables. To address this problem, we propose a latent space model for bipartite networks by incorporating information from both the covariates and the network structure. To reflect the evolution pattern of the network structure, we introduce two parameters representing the persistence effect. To estimate the model, we propose a computationally efficient algorithm using the projected gradient descent method. The theoretical properties are also established and validated through comprehensive simulation studies. Last, we analyze the dynamic bipartite network for the Chinese interlocking directorates from 2010 to 2020 using our proposed model.

Suggested Citation

  • Yan Zhang & Rui Pan & Feifei Wang & Kuangnan Fang & Hansheng Wang, 2025. "Network embedding for bipartite networks with applications in interlocking directorates in Chinese companies," Computational Statistics, Springer, vol. 40(8), pages 4331-4366, November.
    • Handle: RePEc:spr:compst:v:40:y:2025:i:8:d:10.1007_s00180-025-01626-1
    DOI: 10.1007/s00180-025-01626-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00180-025-01626-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00180-025-01626-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Nai H. Lamb & Philip Roundy, 2016. "The “ties that bind” board interlocks research: a systematic review," Management Research Review, Emerald Group Publishing Limited, vol. 39(11), pages 1516-1542, November.
    2. Yaoming Zhen & Junhui Wang, 2023. "Community Detection in General Hypergraph Via Graph Embedding," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 118(543), pages 1620-1629, July.
    3. Nai H. Lamb & Philip Roundy, 2016. "The “ties that bind” board interlocks research: a systematic review," Management Research Review, Emerald Group Publishing Limited, vol. 39(11), pages 1516-1542, November.
    4. Mark S. Handcock & Adrian E. Raftery & Jeremy M. Tantrum, 2007. "Model‐based clustering for social networks," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(2), pages 301-354, March.
    5. Tianxi Li & Elizaveta Levina & Ji Zhu, 2020. "Network cross-validation by edge sampling," Biometrika, Biometrika Trust, vol. 107(2), pages 257-276.
    6. Huang, Danyang & Wang, Feifei & Zhu, Xuening & Wang, Hansheng, 2020. "Two-mode network autoregressive model for large-scale networks," Journal of Econometrics, Elsevier, vol. 216(1), pages 203-219.
    7. Daniel K. Sewell & Yuguo Chen, 2015. "Latent Space Models for Dynamic Networks," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1646-1657, December.
    8. Xuefei Zhang & Gongjun Xu & Ji Zhu, 2022. "Joint latent space models for network data with high-dimensional node variables [Statistical inference on random dot product graphs: a survey]," Biometrika, Biometrika Trust, vol. 109(3), pages 707-720.
    9. Peter D. Hoff, 2005. "Bilinear Mixed-Effects Models for Dyadic Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 286-295, March.
    10. P W MacDonald & E Levina & J Zhu, 2022. "Latent space models for multiplex networks with shared structure [Inference for multiple heterogeneous networks with a common invariant subspace]," Biometrika, Biometrika Trust, vol. 109(3), pages 683-706.
    Full references (including those not matched with items on IDEAS)

    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. Fan, Xinyan & Fang, Kuangnan & Pu, Dan & Qin, Ruixuan, 2024. "Generalized latent space model for one-mode networks with awareness of two-mode networks," Computational Statistics & Data Analysis, Elsevier, vol. 193(C).
    2. Adrian E. Raftery, 2017. "Comment: Extending the Latent Position Model for Networks," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1531-1534, October.
    3. Sosa, Juan & Betancourt, Brenda, 2022. "A latent space model for multilayer network data," Computational Statistics & Data Analysis, Elsevier, vol. 169(C).
    4. Selena Wang & Subhadeep Paul & Paul Boeck, 2023. "Joint Latent Space Model for Social Networks with Multivariate Attributes," Psychometrika, Springer;The Psychometric Society, vol. 88(4), pages 1197-1227, December.
    5. Samrachana Adhikari & Tracy Sweet & Brian Junker, 2021. "Analysis of longitudinal advice‐seeking networks following implementation of high stakes testing," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1475-1500, October.
    6. Chen, Mingli & Fernández-Val, Iván & Weidner, Martin, 2021. "Nonlinear factor models for network and panel data," Journal of Econometrics, Elsevier, vol. 220(2), pages 296-324.
    7. Alex Centeno, 2022. "A Structural Model for Detecting Communities in Networks," Papers 2209.08380, arXiv.org, revised Oct 2022.
    8. Wajdi Ben Rejeb & Sarra Berraies, 2025. "Expanding overseas: the contribution of board role performance from an emerging market perspective," Review of Managerial Science, Springer, vol. 19(12), pages 3847-3881, December.
    9. Pan, Rui & Gao, Yuan & Wang, Hansheng, 2026. "A latent space model for link prediction in statistical citation network," Journal of Multivariate Analysis, Elsevier, vol. 212(C).
    10. Yingda Lu & Kinshuk Jerath & Param Vir Singh, 2013. "The Emergence of Opinion Leaders in a Networked Online Community: A Dyadic Model with Time Dynamics and a Heuristic for Fast Estimation," Management Science, INFORMS, vol. 59(8), pages 1783-1799, August.
    11. Babkin, Sergii & Stewart, Jonathan R. & Long, Xiaochen & Schweinberger, Michael, 2020. "Large-scale estimation of random graph models with local dependence," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
    12. Michael Braun & André Bonfrer, 2011. "Scalable Inference of Customer Similarities from Interactions Data Using Dirichlet Processes," Marketing Science, INFORMS, vol. 30(3), pages 513-531, 05-06.
    13. Tyler H. McCormick & Tian Zheng, 2015. "Latent Surface Models for Networks Using Aggregated Relational Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1684-1695, December.
    14. Patacchini, Eleonora & Hsieh, Chih-Sheng & Lin, Xu, 2019. "Social Interaction Methods," CEPR Discussion Papers 14141, Centre for Economic Policy Research.
    15. Amanda M. Y. Chu & Thomas W. C. Chan & Mike K. P. So & Wing-Keung Wong, 2021. "Dynamic Network Analysis of COVID-19 with a Latent Pandemic Space Model," IJERPH, MDPI, vol. 18(6), pages 1-22, March.
    16. Su, Wenqing & Guo, Xiao & Chang, Xiangyu & Yang, Ying, 2024. "Spectral co-clustering in multi-layer directed networks," Computational Statistics & Data Analysis, Elsevier, vol. 198(C).
    17. Jina Park & Ick Hoon Jin & Minjeong Jeon, 2023. "How Social Networks Influence Human Behavior: An Integrated Latent Space Approach for Differential Social Influence," Psychometrika, Springer;The Psychometric Society, vol. 88(4), pages 1529-1555, December.
    18. Jean-Jacques Daudin & Laurent Pierre & Corinne Vacher, 2010. "Model for Heterogeneous Random Networks Using Continuous Latent Variables and an Application to a Tree–Fungus Network," Biometrics, The International Biometric Society, vol. 66(4), pages 1043-1051, December.
    19. repec:plo:pone00:0071293 is not listed on IDEAS
    20. Daniele Durante & David B. Dunson & Joshua T. Vogelstein, 2017. "Nonparametric Bayes Modeling of Populations of Networks," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1516-1530, October.
    21. Haiyan Liu & Ick Hoon Jin & Zhiyong Zhang & Ying Yuan, 2021. "Social Network Mediation Analysis: A Latent Space Approach," Psychometrika, Springer;The Psychometric Society, vol. 86(1), pages 272-298, March.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:spr:compst:v:40:y:2025:i:8:d:10.1007_s00180-025-01626-1. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.