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Bayesian analysis of directed graphs data with applications to social networks

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  • Paramjit S. Gill
  • Tim B. Swartz

Abstract

Summary. A fully Bayesian analysis of directed graphs, with particular emphasis on applica‐ tions in social networks, is explored. The model is capable of incorporating the effects of covariates, within and between block ties and multiple responses. Inference is straightforward by using software that is based on Markov chain Monte Carlo methods. Examples are provided which highlight the variety of data sets that can be entertained and the ease with which they can be analysed.

Suggested Citation

  • Paramjit S. Gill & Tim B. Swartz, 2004. "Bayesian analysis of directed graphs data with applications to social networks," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 53(2), pages 249-260, April.
    • Handle: RePEc:bla:jorssc:v:53:y:2004:i:2:p:249-260
    DOI: 10.1046/j.1467-9876.2003.05215.x
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    Cited by:

    1. Moebert, Jochen & Tydecks, Patrick, 2007. "Power and Ownership Structures among German Companies. A Network Analysis of Financial Linkages," Darmstadt Discussion Papers in Economics 179, Darmstadt University of Technology, Department of Law and Economics.
    2. Möbert, Jochen & Tydecks, Patrick, 2007. "Power and Ownership Structures among German Companies. A Network Analysis of Financial Linkages," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 35974, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).

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