Exploiting total unimodularity for classes of random network problems
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Inclou dades d'ús des de 2022
Cita com:
hdl:2117/21031
Tipus de documentReport de recerca
Data publicació2013-07
Condicions d'accésAccés obert
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Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
Abstract
Network analysis is of great interest for the study of social
, biological and technolog-
ical networks, with applications, among others, in busines
s, marketing, epidemiology and
telecommunications. Researchers are often interested in a
ssessing whether an observed fea-
ture in some particular network is expected to be found withi
n families of networks under
some hypothesis (named conditional random networks, i.e.,
networks satisfying some linear
constraints). This work presents procedures to generate ne
tworks with specified structural
properties which rely on the solution of classes of integer o
ptimization problems. We show
that, for many of them, the constraints matrices are totally
unimodular, allowing the efficient
generation of conditional random networks by polynomial ti
me interior-point methods. The
computational results suggest that the proposed methods ca
n represent a general framework
for the efficient generation of random networks even beyond the
models analyzed in this pa-
per. This work also opens the possibility for other applicat
ions of mathematical programming
in the analysis of complex networks.
CitacióCastro, J.; Nasini, S. "Exploiting total unimodularity for classes of random network problems". 2013.
Forma partDR2013-01
URL repositori externhttp://www-eio.upc.es/~jcastro/publications/reports/dr2013-01.pdf
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