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A simulated annealing heuristic for maximum correlation core/periphery partitioning of binary networks
- Source :
- PLoS ONE, PLoS ONE, Vol 12, Iss 5, p e0170448 (2017)
- Publication Year :
- 2017
- Publisher :
- Public Library of Science, 2017.
-
Abstract
- 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.
- Subjects :
- Proteomics
Optimization problem
Computer science
Binary number
lcsh:Medicine
Social Sciences
Adaptive simulated annealing
01 natural sciences
Biochemistry
010104 statistics & probability
0504 sociology
Sociology
Psychology
Heuristics
lcsh:Science
Simulated Annealing
Problem Solving
Multidisciplinary
Heuristic
Applied Mathematics
Simulation and Modeling
05 social sciences
Core (game theory)
Social Networks
Simulated annealing
Physical Sciences
Protein Interaction Networks
Algorithms
Network Analysis
Research Article
Optimization
Mathematical optimization
Computer and Information Sciences
Heuristic (computer science)
Research and Analysis Methods
Set (abstract data type)
Computer Software
Genetic algorithm
Computer Simulation
0101 mathematics
Genetic Algorithms
lcsh:R
Cognitive Psychology
050401 social sciences methods
Biology and Life Sciences
Maximization
Cognitive Science
lcsh:Q
Mathematics
Neuroscience
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 12
- Issue :
- 5
- Database :
- OpenAIRE
- Journal :
- PLoS ONE
- Accession number :
- edsair.doi.dedup.....d255912307128cdcde66d146355664d3