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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. (en)
Title
  • Genetic algorithm for sampling from scale-free data and networks
  • Genetic algorithm for sampling from scale-free data and networks (en)
skos:prefLabel
  • Genetic algorithm for sampling from scale-free data and networks
  • Genetic algorithm for sampling from scale-free data and networks (en)
skos:notation
  • RIV/61989100:27240/14:86092521!RIV15-MSM-27240___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(EE.2.3.20.0073), S
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
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http://linked.open...dnocenehoVysledku
  • 18085
http://linked.open...ai/riv/idVysledku
  • RIV/61989100:27240/14:86092521
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Sampling; Power law; Networks; Genetic algorithms; Data (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [1CA187ED7ACB]
http://linked.open...v/mistoKonaniAkce
  • Vancouver
http://linked.open...i/riv/mistoVydani
  • New York
http://linked.open...i/riv/nazevZdroje
  • GECCO 2014 - Proceedings of the 2014 Genetic and Evolutionary Computation Conference
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Krömer, Pavel
  • Platoš, Jan
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
http://bibframe.org/vocab/doi
  • 10.1145/2576768.2598391
http://purl.org/ne...btex#hasPublisher
  • ACM
https://schema.org/isbn
  • 978-1-4503-2662-9
http://localhost/t...ganizacniJednotka
  • 27240
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