A simulated annealing heuristic for maximum correlation core/periphery partitioning of binary networks

A popular objective criterion for partitioning a set of actors into core and periphery subsets is the maximization of the correlation between an ideal and observed structure associated with intra-core and intra-periphery ties. The resulting optimization problem has commonly been tackled using heuris...

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Bibliographic Details
Published in:	PloS one 2017-05, Vol.12 (5), p.e0170448-e0170448
Main Authors:	Brusco, Michael, Stolze, Hannah J, Hoffman, Michaela, Steinley, Douglas
Format:	Article
Language:	eng
Subjects:	Algorithms Binary system Biology and Life Sciences Computer and Information Sciences Computer Simulation Correlation Discriminant analysis Genetic algorithms Heuristic Heuristic methods Heuristic programming Heuristics Optimization Physical Sciences Principal components analysis Problem solving Relocation Research and Analysis Methods Simulated annealing (Mathematics) Social network analysis Social networks Social Sciences
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Summary:	A popular objective criterion for partitioning a set of actors into core and periphery subsets is the maximization of the correlation between an ideal and observed structure associated with intra-core and intra-periphery ties. The resulting optimization problem has commonly been tackled using heuristic procedures such as relocation algorithms, genetic algorithms, and simulated annealing. In this paper, we present a computationally efficient simulated annealing algorithm for maximum correlation core/periphery partitioning of binary networks. The algorithm is evaluated using simulated networks consisting of up to 2000 actors and spanning a variety of densities for the intra-core, intra-periphery, and inter-core-periphery components of the network. Core/periphery analyses of problem solving, trust, and information sharing networks for the frontline employees and managers of a consumer packaged goods manufacturer are provided to illustrate the use of the model.
ISSN:	1932-6203 1932-6203