Abstract

Network models are widely used to represent relational information among interacting units. In studies of social networks, recent emphasis has been placed on random graph models where the nodes usually represent individual social actors and the edges represent the presence of a specified relation between actors. We develop a class of models where the probability of a relation between actors depends on the positions of individuals in an unobserved "social space." We make inference for the social space within maximum likelihood and Bayesian frameworks, and propose Markov chain Monte Carlo procedures for making inference on latent positions and the effects of observed covariates. We present analyses of three standard datasets from the social networks literature, and compare the method to an alternative stochastic blockmodeling approach. In addition to improving on model fit for these datasets, our method provides a visual and interpretable model-based spatial representation of social relationships and improves on existing methods by allowing the statistical uncertainty in the social space to be quantified and graphically represented.

Keywords

InferenceComputer scienceMarkov chain Monte CarloCovariateSocial network analysisRelation (database)Social network (sociolinguistics)Bayesian inferenceSpace (punctuation)Latent class modelStatistical inferenceMarkov chainBayesian probabilityExponential random graph modelsGraphData miningTheoretical computer scienceMachine learningArtificial intelligenceRandom graphMathematicsStatistics

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Publication Info

Year
2002
Type
article
Volume
97
Issue
460
Pages
1090-1098
Citations
1888
Access
Closed

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Peter D. Hoff, Adrian E. Raftery, Mark S. Handcock (2002). Latent Space Approaches to Social Network Analysis. Journal of the American Statistical Association , 97 (460) , 1090-1098. https://doi.org/10.1198/016214502388618906

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DOI
10.1198/016214502388618906