Generation of networks with prescribed degree-dependent clustering |
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Authors: | Chrysanthos E Gounaris Karthikeyan Rajendran Ioannis G Kevrekidis Christodoulos A Floudas |
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Institution: | (2) Complex Systems Lab, Parc de Recerca Biomedica de Barcelona, Barcelona, Spain;(3) Santa Fe Institute, Santa Fe, USA; |
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Abstract: | We propose a systematic, rigorous mathematical optimization methodology for the construction, “on demand,” of network structures
that are guaranteed to possess a prescribed collective property: the degree-dependent clustering. The ability to generate
such realizations of networks is important not only for creating artificial networks that can perform desired functions, but
also to facilitate the study of networks as part of other algorithms. This problem exhibits large combinatorial complexity
and is difficult to solve with off-the-shelf commercial optimization software. To that end, we also present a customized preprocessing
algorithm that allows us to judiciously fix certain problem variables and, thus, significantly reduce computational times.
Results from the application of the framework to data sets resulting from simulations of an acquaintance network formation
model are presented. |
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Keywords: | |
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