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Statements

Subject Item
n2:RIV%2F61989100%3A27240%2F14%3A86092521%21RIV15-MSM-27240___
rdf:type
skos:Concept n7:Vysledek
dcterms:description
A variety of real-world data and networks can be described by a heavy-tailed probability distribution of its values, vertex degrees, or other significant properties, that follows the power law. Such a scale-free data and networks can be found in both natural phenomena such as protein interaction networks and gene regulation networks and man-made structures like the Internet, language, and various social networks. An efficient analysis of large scale data and networks is often impractical and various heuristic and metaheuristc sampling techniques are deployed to select smaller subsets of the data for analysis and visualisation. A key goal of data and network sampling is to select such a subset of the original data that would accurately represent the original data with respect to selected attributes. In this work we propose a novel genetic algorithm for scale-free data and network sampling and evaluate the algorithm in a series of computational experiments. A variety of real-world data and networks can be described by a heavy-tailed probability distribution of its values, vertex degrees, or other significant properties, that follows the power law. Such a scale-free data and networks can be found in both natural phenomena such as protein interaction networks and gene regulation networks and man-made structures like the Internet, language, and various social networks. An efficient analysis of large scale data and networks is often impractical and various heuristic and metaheuristc sampling techniques are deployed to select smaller subsets of the data for analysis and visualisation. A key goal of data and network sampling is to select such a subset of the original data that would accurately represent the original data with respect to selected attributes. In this work we propose a novel genetic algorithm for scale-free data and network sampling and evaluate the algorithm in a series of computational experiments.
dcterms:title
Genetic algorithm for sampling from scale-free data and networks Genetic algorithm for sampling from scale-free data and networks
skos:prefLabel
Genetic algorithm for sampling from scale-free data and networks Genetic algorithm for sampling from scale-free data and networks
skos:notation
RIV/61989100:27240/14:86092521!RIV15-MSM-27240___
n4:aktivita
n14:S n14:P
n4:aktivity
P(EE.2.3.20.0073), S
n4:dodaniDat
n8:2015
n4:domaciTvurceVysledku
n6:6026877 n6:9175970
n4:druhVysledku
n12:D
n4:duvernostUdaju
n18:S
n4:entitaPredkladatele
n11:predkladatel
n4:idSjednocenehoVysledku
18085
n4:idVysledku
RIV/61989100:27240/14:86092521
n4:jazykVysledku
n9:eng
n4:klicovaSlova
Sampling; Power law; Networks; Genetic algorithms; Data
n4:klicoveSlovo
n5:Genetic%20algorithms n5:Power%20law n5:Sampling n5:Data n5:Networks
n4:kontrolniKodProRIV
[1CA187ED7ACB]
n4:mistoKonaniAkce
Vancouver
n4:mistoVydani
New York
n4:nazevZdroje
GECCO 2014 - Proceedings of the 2014 Genetic and Evolutionary Computation Conference
n4:obor
n10:IN
n4:pocetDomacichTvurcuVysledku
2
n4:pocetTvurcuVysledku
2
n4:projekt
n22:EE.2.3.20.0073
n4:rokUplatneniVysledku
n8:2014
n4:tvurceVysledku
Krömer, Pavel Platoš, Jan
n4:typAkce
n20:WRD
n4:zahajeniAkce
2014-07-12+02:00
s:numberOfPages
8
n19:doi
10.1145/2576768.2598391
n13:hasPublisher
ACM
n17:isbn
978-1-4503-2662-9
n21:organizacniJednotka
27240