Skip to main content

Designing networks: A mixed-integer linear optimization approach

Author(s): Gounaris, Chrysanthos E.; Rajendran, Karthikeyan; Kevrekidis, Yannis G.; Floudas, Christodoulos A.

To refer to this page use:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorGounaris, Chrysanthos E.-
dc.contributor.authorRajendran, Karthikeyan-
dc.contributor.authorKevrekidis, Yannis G.-
dc.contributor.authorFloudas, Christodoulos A.-
dc.identifier.citationGounaris, Chrysanthos E, Rajendran, Karthikeyan, Kevrekidis, Yannis G, Floudas, Christodoulos A. (2016). Designing networks: A mixed-integer linear optimization approach. Networks, 68 (4), 283 - 301. doi:10.1002/net.21699en_US
dc.description.abstractDesigning networks with specified collective properties is useful in a variety of application areas, enabling the study of how given properties affect the behavior of network models, the downscaling of empirical networks to workable sizes, and the analysis of network evolution. Despite the importance of the task, there currently exists a gap in our ability to systematically generate networks that adhere to theoretical guarantees for the given property specifications. In this paper, we propose the use of Mixed-Integer Linear Optimization modeling and solution methodologies to address this Network Generation Problem. We present a number of useful modeling techniques and apply them to mathematically express and constrain network properties in the context of an optimization formulation. We then develop complete formulations for the generation of networks that attain specified levels of connectivity, spread, assortativity and robustness, and we illustrate these via a number of computational case studies.en_US
dc.format.extent283 - 301en_US
dc.rightsAuthor's manuscripten_US
dc.titleDesigning networks: A mixed-integer linear optimization approachen_US
dc.typeJournal Articleen_US

Files in This Item:
File Description SizeFormat 
Designing_networks_mixed_integer_approach.pdf608.54 kBAdobe PDFView/Download

Items in OAR@Princeton are protected by copyright, with all rights reserved, unless otherwise indicated.