IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v193y2024ics0167947323002268.html
   My bibliography  Save this article

Generalized latent space model for one-mode networks with awareness of two-mode networks

Author

Listed:
  • Fan, Xinyan
  • Fang, Kuangnan
  • Pu, Dan
  • Qin, Ruixuan

Abstract

Latent space models have been widely studied for one-mode networks, in which the same type of nodes connect with each other. In many applications, one-mode networks are often observed along with two-mode networks, which reflect connections between different types of nodes and provide important information for understanding the one-mode network structure. However, the classical one-mode latent space models have several limitations in incorporating two-mode networks. To address this gap, a generalized latent space model is proposed to capture common structures and heterogeneous connecting patterns across one-mode and two-mode networks. Specifically, each node is embedded with a latent vector and network-specific degree parameters that determine the connection probabilities between nodes. A projected gradient descent algorithm is developed to estimate the latent vectors and degree parameters. Moreover, the theoretical properties of the estimators are established and it has been proven that the estimation accuracy of the shared latent vectors can be improved through incorporating two-mode networks. Finally, simulation studies and applications on two real-world datasets demonstrate the usefulness of the proposed model.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:csdana:v:193:y:2024:i:c:s0167947323002268
    DOI: 10.1016/j.csda.2023.107915
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947323002268
    Download Restriction: Full text for ScienceDirect subscribers only.
    File URL: https://libkey.io/10.1016/j.csda.2023.107915?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Härdle, Wolfgang Karl & Wang, Weining & Yu, Lining, 2016. "TENET: Tail-Event driven NETwork risk," Journal of Econometrics, Elsevier, vol. 192(2), pages 499-513.
    2. Feng, Liang & Zhou, Cangqi & Zhao, Qianchuan, 2019. "A spectral method to find communities in bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 424-437.
    3. Yujia Wu & Wei Lan & Tao Zou & Chih-Ling Tsai, 2022. "Inward and Outward Network Influence Analysis," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(4), pages 1617-1628, October.
    4. Billio, Monica & Getmansky, Mila & Lo, Andrew W. & Pelizzon, Loriana, 2012. "Econometric measures of connectedness and systemic risk in the finance and insurance sectors," Journal of Financial Economics, Elsevier, vol. 104(3), pages 535-559.
    5. 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.
    6. Jingnan Zhang & Xin He & Junhui Wang, 2022. "Directed Community Detection With Network Embedding," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 117(540), pages 1809-1819, October.
    7. 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.
    8. Chabert-Liddell, Saint-Clair & Barbillon, Pierre & Donnet, Sophie & Lazega, Emmanuel, 2021. "A stochastic block model approach for the analysis of multilevel networks: An application to the sociology of organizations," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).
    9. 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.
    10. Jingfei Zhang & Will Wei Sun & Lexin Li, 2020. "Mixed-Effect Time-Varying Network Model and Application in Brain Connectivity Analysis," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(532), pages 2022-2036, December.
    11. Lam, Clifford & Yao, Qiwei, 2012. "Factor modeling for high-dimensional time series: inference for the number of factors," LSE Research Online Documents on Economics 45684, London School of Economics and Political Science, LSE Library.
    12. Mingyang Ren & Sanguo Zhang & Junhui Wang, 2023. "Consistent estimation of the number of communities via regularized network embedding," Biometrics, The International Biometric Society, vol. 79(3), pages 2404-2416, September.
    13. Härdle, Wolfgang Karl & Sirotko-Sibirskaya, Natalia & Wang, Weining, 2014. "TENET: Tail-Event driven NETwork risk," SFB 649 Discussion Papers 2014-066, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    14. 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.
    15. 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.
    16. Pierre Barbillon & Sophie Donnet & Emmanuel Lazega & Avner Bar-Hen, 2017. "Stochastic block models for multiplex networks: an application to a multilevel network of researchers," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(1), pages 295-314, January.
    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. Zongwu Cai & Xiyuan Liu, 2020. "A Functional-Coefficient VAR Model for Dynamic Quantiles with Constructing Financial Network," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202017, University of Kansas, Department of Economics, revised Oct 2020.
    2. Zhu, Xuening & Wang, Weining & Wang, Hangsheng & Härdle, Wolfgang Karl, 2016. "Network quantile autoregression," SFB 649 Discussion Papers 2016-050, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    3. Zhu, Xuening & Wang, Weining & Wang, Hansheng & Härdle, Wolfgang Karl, 2019. "Network quantile autoregression," Journal of Econometrics, Elsevier, vol. 212(1), pages 345-358.
    4. Shi, Huai-Long & Zhou, Wei-Xing, 2022. "Factor volatility spillover and its implications on factor premia," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 80(C).
    5. Ouyang, Zisheng & Zhou, Xuewei & Lu, Min & Liu, Ke, 2024. "Imported financial risk in global stock markets: Evidence from the interconnected network," Research in International Business and Finance, Elsevier, vol. 69(C).
    6. Addi, Abdelhamid & Bouoiyour, Jamal, 2023. "Interconnectedness and extreme risk: Evidence from dual banking systems," Economic Modelling, Elsevier, vol. 120(C).
    7. Ahmad, Wasim & Tiwari, Shiv Ratan & Wadhwani, Akshay & Khan, Mohammad Azeem & Bekiros, Stelios, 2023. "Financial networks and systemic risk vulnerabilities: A tale of Indian banks," Research in International Business and Finance, Elsevier, vol. 65(C).
    8. Chen, Bin-xia & Sun, Yan-lin, 2024. "Financial market connectedness between the U.S. and China: A new perspective based on non-linear causality networks," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 90(C).
    9. Yang, Xin & Wen, Shigang & Zhao, Xian & Huang, Chuangxia, 2020. "Systemic importance of financial institutions: A complex network perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    10. Zhang, Weiping & Zhuang, Xintian & Wang, Jian & Lu, Yang, 2020. "Connectedness and systemic risk spillovers analysis of Chinese sectors based on tail risk network," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    11. Dai, Zhifeng & Tang, Rui & Zhang, Xiaotong, 2023. "A new multilayer network for measuring interconnectedness among the energy firms," Energy Economics, Elsevier, vol. 124(C).
    12. Wang, Gang-Jin & Chen, Yang-Yang & Si, Hui-Bin & Xie, Chi & Chevallier, Julien, 2021. "Multilayer information spillover networks analysis of China’s financial institutions based on variance decompositions," International Review of Economics & Finance, Elsevier, vol. 73(C), pages 325-347.
    13. Daniel Felix Ahelegbey & Paolo Giudici, 2020. "Market Risk, Connectedness and Turbulence: A Comparison of 21st Century Financial Crises," DEM Working Papers Series 188, University of Pavia, Department of Economics and Management.
    14. Dai, Zhifeng & Tang, Rui & Zhang, Xinhua, 2023. "Multilayer network analysis for measuring the inter-connectedness between the oil market and G20 stock markets," Energy Economics, Elsevier, vol. 120(C).
    15. Daniel Felix Ahelegbey, 2022. "Statistical Modelling of Downside Risk Spillovers," FinTech, MDPI, vol. 1(2), pages 1-10, April.
    16. Zhang, Weiping & Zhuang, Xintian & Lu, Yang & Wang, Jian, 2020. "Spatial linkage of volatility spillovers and its explanation across G20 stock markets: A network framework," International Review of Financial Analysis, Elsevier, vol. 71(C).
    17. Ling, Yu-Xiu & Xie, Chi & Wang, Gang-Jin, 2022. "Interconnectedness between convertible bonds and underlying stocks in the Chinese capital market: A multilayer network perspective," Emerging Markets Review, Elsevier, vol. 52(C).
    18. Matteo Barigozzi & Christian Brownlees, 2019. "NETS: Network estimation for time series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(3), pages 347-364, April.
    19. Jozef Barunik & Mattia Bevilacqua & Radu Tunaru, 2022. "Asymmetric Network Connectedness of Fears," The Review of Economics and Statistics, MIT Press, vol. 104(6), pages 1304-1316, November.
    20. Habibi, Hamidreza & Mohammadi, Hassan, 2022. "Return and volatility spillovers across the Western and MENA countries," The North American Journal of Economics and Finance, Elsevier, vol. 60(C).

    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:eee:csdana:v:193:y:2024:i:c:s0167947323002268. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    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.