Premium
Modeling the dynamics of social networks using Bayesian hierarchical blockmodels
Author(s) -
Rodríguez Abel
Publication year - 2012
Publication title -
statistical analysis and data mining: the asa data science journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.381
H-Index - 33
eISSN - 1932-1872
pISSN - 1932-1864
DOI - 10.1002/sam.10150
Subject(s) - homophily , computer science , cluster analysis , transitive relation , bayesian probability , variable order bayesian network , inference , data mining , bayesian inference , machine learning , prior probability , artificial intelligence , hierarchical clustering , nonparametric statistics , class (philosophy) , econometrics , mathematics , combinatorics
We introduce a new class of dynamic models for networks that extends stochastic blockmodels to settings where the interactions between a group of actors are observed at multiple points in time. Our goal is to identify structural changes in model features such as differential attachment, homophily by attributes, transitivity, and clustering as the network evolves. Our focus is on Bayesian inference, so the models are constructed hierarchically by combining different classes of Bayesian nonparametric priors. The methods are illustrated through a simulation study and two real data sets. © 2011 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 5: 218–234, 2012