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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
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