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Bayesian analysis of directed graphs data with applications to social networks
Author(s) -
Gill Paramjit S.,
Swartz Tim B.
Publication year - 2004
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1046/j.1467-9876.2003.05215.x
Subject(s) - computer science , variety (cybernetics) , markov chain monte carlo , variable order bayesian network , inference , bayesian probability , data mining , theoretical computer science , markov chain , machine learning , bayesian network , covariate , bayesian inference , artificial intelligence , data science
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.