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A variable neighborhood search method for a two-mode blockmodeling problem in social network analysis

Published online by Cambridge University Press:  30 July 2013

MICHAEL BRUSCO ,
PATRICK DOREIAN ,
PAULETTE LLOYD  and
DOUGLAS STEINLEY
MICHAEL BRUSCO
Affiliation:
College of Business, Florida State University, Tallahassee, FL, USA (e-mail: [email protected])
PATRICK DOREIAN
Affiliation:
Department of Sociology, University of Pittsburgh, Pittsburgh, PA, USA Faculty of Social Sciences, University of Ljubljana, Ljubljana, Slovenia
PAULETTE LLOYD
Affiliation:
AAAS Fellow and US Department of State, Washington, DC, USA
DOUGLAS STEINLEY
Affiliation:
Department of Psychological Sciences, University of Missouri–Columbia, Columbia, MO, USA
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Abstract

This paper presents a variable neighborhood search (VNS) algorithm that is specially designed for the blockmodeling of two-mode binary network matrices in accordance with structural equivalence. Computational results for 768 synthetic test networks revealed that the VNS heuristic outperformed a relocation heuristic (RH) and a tabu search (TS) method for the same problem. Next, the three heuristics were applied to two-mode network data pertaining to the votes of member countries on resolutions in the United Nations General Assembly. A comparative analysis revealed that the VNS heuristic often provided slightly better criterion function values than RH and TS, and that these small differences in criterion function values could sometimes be associated with substantial differences in the actual partitions obtained. Overall, the results suggest that the VNS heuristic is a promising approach for blockmodeling of two-mode binary networks. Recommendations for extensions to stochastic blockmodeling applications are provided.

Keywords

deterministic blockmodelingtwo-mode binary networksheuristicsvariable neighborhood searchUNGA voting data
Type
Research Article
Information
Network Science , Volume 1 , Issue 2 , August 2013 , pp. 191 - 212
Copyright
Copyright © Cambridge University Press 2013 

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