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A note on testing conditional independence for social network analysis

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Abstract

In social network analysis, logistic regression models have been widely used to establish the relationship between the response variable and covariates. However, such models often require the network relationships to be mutually independent, after controlling for a set of covariates. To assess the validity of this assumption, we propose test statistics, under the logistic regression setting, for three important social network drivers. They are, respectively, reciprocity, centrality, and transitivity. The asymptotic distributions of those test statistics are obtained. Extensive simulation studies are also presented to demonstrate their finite sample performance and usefulness.

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Authors and Affiliations

  1. School of Statistics and Mathematics, Central University of Finance and Economics, Beijing, 100081, China

    Rui Pan

  2. Guanghua School of Management, Peking University, Beijing, 100871, China

    HanSheng Wang

Authors
  1. Rui Pan
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  2. HanSheng Wang
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Correspondence to Rui Pan.

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Pan, R., Wang, H. A note on testing conditional independence for social network analysis. Sci. China Math. 58, 1179–1190 (2015). https://doi.org/10.1007/s11425-015-4998-0

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  • DOI: https://doi.org/10.1007/s11425-015-4998-0

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