title: Multiobjective blockmodeling for social network analysis.
abstract: To date, most methods for direct blockmodeling of social network data have focused on the optimization of a single objective function. However, there are a variety of social network applications where it is advantageous to consider two or more objectives simultaneously. These applications can broadly be placed into two categories: (1) simultaneous optimization of multiple criteria for fitting a blockmodel based on a single network matrix and (2) simultaneous optimization of multiple criteria for fitting a blockmodel based on two or more network matrices, where the matrices being fit can take the form of multiple indicators for an underlying relationship, or multiple matrices for a set of objects measured at two or more different points in time. A multiobjective tabu search procedure is proposed for estimating the set of Pareto efficient blockmodels. This procedure is used in three examples that demonstrate possible applications of the multiobjective blockmodeling paradigm.
authors.affiliation: College of Business, Florida State University, Tallahassee, FL, 32306-1110, USA, mbrusco@fsu.edu.
authors.forename: Michael
authors.initials: M
authors.lastname: Brusco
authors.forename: Patrick
authors.initials: P
authors.lastname: Doreian
authors.forename: Douglas
authors.initials: D
authors.lastname: Steinley
authors.forename: Cinthia B
authors.initials: CB
authors.lastname: Satornino
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D008297:Male; D008960:Models, Psychological; D065830:Monks / Q000523:psychology; D012944:Social Support
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