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Bipartite network influence analysis of a two-mode network

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  • Wu, Yujia
  • Lan, Wei
  • Fan, Xinyan
  • Fang, Kuangnan

Abstract

A two-mode network contains two types of nodes, and edges exist only between any two nodes that are associated with different entities. Owing to the network connections (i.e., edges) between the two types of network nodes, nodal responses are unlikely to be independently and identically distributed, resulting in possible nodal heterogeneity across the two types of nodes. This study proposes a novel bipartite network influence model (BNIM) to evaluate nodal heterogeneity from the perspective of nodal influence. To make the model estimable, we parameterize the influence indices with a set of nodal attributes through a prespecified link function, and employ the quasi-maximum likelihood approach to estimate the unknown parameters. Score tests are presented to examine the heterogeneity of nodal influences across the two types of nodes. To assess the adequacy of the link function, we carry out a quasi-likelihood ratio test and establish its asymptotic properties under the appropriate conditions. Simulation studies and the real data analysis of a fund-stock network are studied to assess the finite-sample performance of BNIM.

Suggested Citation

  • Wu, Yujia & Lan, Wei & Fan, Xinyan & Fang, Kuangnan, 2024. "Bipartite network influence analysis of a two-mode network," Journal of Econometrics, Elsevier, vol. 239(2).
  • Handle: RePEc:eee:econom:v:239:y:2024:i:2:s0304407623002786
    DOI: 10.1016/j.jeconom.2023.105562
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    as
    1. Gupta, Abhimanyu & Robinson, Peter M., 2018. "Pseudo maximum likelihood estimation of spatial autoregressive models with increasing dimension," Journal of Econometrics, Elsevier, vol. 202(1), pages 92-107.
    2. Luca Marotta & Salvatore Miccichè & Yoshi Fujiwara & Hiroshi Iyetomi & Hideaki Aoyama & Mauro Gallegati & Rosario N Mantegna, 2015. "Bank-Firm Credit Network in Japan: An Analysis of a Bipartite Network," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-18, May.
    3. Rob Alessie & Stefan Hochguertel & Arthur van Soest, 2004. "Ownership of Stocks and Mutual Funds: A Panel Data Analysis," The Review of Economics and Statistics, MIT Press, vol. 86(3), pages 783-796, August.
    4. Clifford Lam & Pedro C.L. Souza, 2020. "Estimation and Selection of Spatial Weight Matrix in a Spatial Lag Model," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(3), pages 693-710, July.
    5. Gao, Zhaoxing & Ma, Yingying & Wang, Hansheng & Yao, Qiwei, 2019. "Banded spatio-temporal autoregressions," Journal of Econometrics, Elsevier, vol. 208(1), pages 211-230.
    6. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    7. Gupta, Abhimanyu & Robinson, Peter M., 2015. "Inference on higher-order spatial autoregressive models with increasingly many parameters," Journal of Econometrics, Elsevier, vol. 186(1), pages 19-31.
    8. An, Pengli & Li, Huajiao & Zhou, Jinsheng & Chen, Fan, 2017. "The evolution analysis of listed companies co-holding non-listed financial companies based on two-mode heterogeneous networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 484(C), pages 558-568.
    9. Jing Zhou & Yundong Tu & Yuxin Chen & Hansheng Wang, 2017. "Estimating Spatial Autocorrelation With Sampled Network Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(1), pages 130-138, January.
    10. Malikov, Emir & Sun, Yiguo, 2017. "Semiparametric estimation and testing of smooth coefficient spatial autoregressive models," Journal of Econometrics, Elsevier, vol. 199(1), pages 12-34.
    11. Clarke, Kevin A., 2007. "A Simple Distribution-Free Test for Nonnested Model Selection," Political Analysis, Cambridge University Press, vol. 15(3), pages 347-363, July.
    12. Zan Huang & Daniel D. Zeng & Hsinchun Chen, 2007. "Analyzing Consumer-Product Graphs: Empirical Findings and Applications in Recommender Systems," Management Science, INFORMS, vol. 53(7), pages 1146-1164, July.
    13. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
    14. Carhart, Mark M, 1997. "On Persistence in Mutual Fund Performance," Journal of Finance, American Finance Association, vol. 52(1), pages 57-82, March.
    15. Souma, Wataru & Fujiwara, Yoshi & Aoyama, Hideaki, 2003. "Complex networks and economics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 324(1), pages 396-401.
    16. Franklin Edwards & Xin Zhang, 1998. "Mutual Funds and Stock and Bond Market Stability," Journal of Financial Services Research, Springer;Western Finance Association, vol. 13(3), pages 257-282, June.
    17. Alexakis, Christos & Niarchos, Nikitas & Patra, Theopfano & Poshakwale, Sunil, 2005. "The dynamics between stock returns and mutual fund flows: empirical evidence from the Greek market," International Review of Financial Analysis, Elsevier, vol. 14(5), pages 559-569.
    18. 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.
    19. Qureshi, Fiza & Kutan, Ali M. & Ghafoor, Abdul & Hussain Khan, Habib & Qureshi, Zeeshan, 2019. "Dynamics of mutual funds and stock markets in Asian developing economies," Journal of Asian Economics, Elsevier, vol. 65(C).
    20. Zhu, Xuening & Chang, Xiangyu & Li, Runze & Wang, Hansheng, 2019. "Portal nodes screening for large scale social networks," Journal of Econometrics, Elsevier, vol. 209(2), pages 145-157.
    21. Lung-Fei Lee, 2004. "Asymptotic Distributions of Quasi-Maximum Likelihood Estimators for Spatial Autoregressive Models," Econometrica, Econometric Society, vol. 72(6), pages 1899-1925, November.
    22. Carusi, Chiara & Bianchi, Giuseppe, 2019. "Scientific community detection via bipartite scholar/journal graph co-clustering," Journal of Informetrics, Elsevier, vol. 13(1), pages 354-386.
    23. 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.
    24. Cui, Yaozu & Wang, Xingyuan, 2016. "Detecting one-mode communities in bipartite networks by bipartite clustering triangular," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 307-315.
    25. Dou, Baojun & Parrella, Maria Lucia & Yao, Qiwei, 2016. "Generalized Yule–Walker estimation for spatio-temporal models with unknown diagonal coefficients," LSE Research Online Documents on Economics 67151, London School of Economics and Political Science, LSE Library.
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