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Hierarchical longitudinal models of relationships in social networks
Author(s) -
Paul Sudeshna,
O'Malley A. James
Publication year - 2013
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 - 10.1111/rssc.12013
Subject(s) - dyad , friendship , social network (sociolinguistics) , markov chain monte carlo , transitive relation , markov chain , longitudinal study , computer science , social network analysis , psychology , bayesian probability , econometrics , social psychology , mathematics , statistics , artificial intelligence , machine learning , combinatorics , world wide web , social media
Summary Motivated by the need to understand the dynamics of relationship formation and dissolution over time in real world social networks we develop a new longitudinal model for transitions in the relationship status of pairs of individuals (‘dyads’). We first specify a model for the relationship status of a single dyad and then extend it to account for important interdyad dependences (e.g. transitivity—‘a friend of a friend is a friend’) and heterogeneity. Model parameters are estimated by using Bayesian analysis implemented via Markov chain Monte Carlo sampling. We use the model to perform novel analyses of two diverse longitudinal friendship networks: an excerpt of the Teenage Friends and Lifestyle Study (a moderately sized network) and the Framingham Heart Study (a large network).