Sampling as a Method of Comparing Real and Generated Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F18%3A10238665" target="_blank" >RIV/61989100:27240/18:10238665 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-319-68527-4_13" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-319-68527-4_13</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-68527-4_13" target="_blank" >10.1007/978-3-319-68527-4_13</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Sampling as a Method of Comparing Real and Generated Networks

Popis výsledku v původním jazyce

In this paper, we combine the use of sampling methods and a network generator to assess the degree of similarity between real and generated networks. Generative network models provide a tool for studying essential network features. These include, for example, the average and distribution of node degree, cluster coefficient and community size. The aim of the generators based on these models is to create networks with properties close to real networks. Even with a high similarity of global properties of real and generated networks, the local structures of these networks often differ considerably. On the other hand, when the network is reduced by a sampling method, global features of networks are strongly influenced by local structures. In the paper, we compare properties of a real-world network and a generated network and also properties of their small samples. In experiments, we show how the distribution of the properties of individual networks change by using different sampling methods and how these distributions differ for both networks and their small samples. (C) 2018, Springer International Publishing AG.

Název v anglickém jazyce

Sampling as a Method of Comparing Real and Generated Networks

Popis výsledku anglicky

In this paper, we combine the use of sampling methods and a network generator to assess the degree of similarity between real and generated networks. Generative network models provide a tool for studying essential network features. These include, for example, the average and distribution of node degree, cluster coefficient and community size. The aim of the generators based on these models is to create networks with properties close to real networks. Even with a high similarity of global properties of real and generated networks, the local structures of these networks often differ considerably. On the other hand, when the network is reduced by a sampling method, global features of networks are strongly influenced by local structures. In the paper, we compare properties of a real-world network and a generated network and also properties of their small samples. In experiments, we show how the distribution of the properties of individual networks change by using different sampling methods and how these distributions differ for both networks and their small samples. (C) 2018, Springer International Publishing AG.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Advances in Intelligent Systems and Computing. Volume 682

ISBN

978-3-319-68526-7

ISSN

2194-5357

e-ISSN

neuvedeno

Počet stran výsledku

11

Strana od-do

117-127

Název nakladatele

Springer Verlag

Místo vydání

Cham

Místo konání akce

Málaga

Datum konání akce

9. 10. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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