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Statements

Subject Item
n2:RIV%2F61989100%3A27240%2F14%3A86092470%21RIV15-MSM-27240___
rdf:type
skos:Concept n18:Vysledek
dcterms:description
The main features of current real-world networks are their large sizes and structures, which show varying degrees of importance of the nodes in their surroundings. The topic of evaluating the importance of the nodes offers many different approaches that usually work with unweighted networks. We present a novel, simple and straightforward approach for the evaluation of the network's nodes with a focus on local properties in their surroundings. The presented approach is intended for weighted networks where the weight can be interpreted as the proximity between the nodes. Our suggested x-representativeness then takes into account the degree of the node, its nearest neighbors and one other parameter which we call the x-representativeness base. Following that, we also present experiments with three different real-world networks. The aim of these experiments is to show that the x-representativeness can be used to deterministically reduce the network to differently sized samples of representatives, while maintaining the topological properties of the original network. The main features of current real-world networks are their large sizes and structures, which show varying degrees of importance of the nodes in their surroundings. The topic of evaluating the importance of the nodes offers many different approaches that usually work with unweighted networks. We present a novel, simple and straightforward approach for the evaluation of the network's nodes with a focus on local properties in their surroundings. The presented approach is intended for weighted networks where the weight can be interpreted as the proximity between the nodes. Our suggested x-representativeness then takes into account the degree of the node, its nearest neighbors and one other parameter which we call the x-representativeness base. Following that, we also present experiments with three different real-world networks. The aim of these experiments is to show that the x-representativeness can be used to deterministically reduce the network to differently sized samples of representatives, while maintaining the topological properties of the original network.
dcterms:title
Local representatives in weighted networks Local representatives in weighted networks
skos:prefLabel
Local representatives in weighted networks Local representatives in weighted networks
skos:notation
RIV/61989100:27240/14:86092470!RIV15-MSM-27240___
n3:aktivita
n11:S n11:P
n3:aktivity
P(ED1.1.00/02.0070), S
n3:dodaniDat
n15:2015
n3:domaciTvurceVysledku
n16:8920796 n16:7845782 n16:6026877
n3:druhVysledku
n6:D
n3:duvernostUdaju
n17:S
n3:entitaPredkladatele
n14:predkladatel
n3:idSjednocenehoVysledku
26461
n3:idVysledku
RIV/61989100:27240/14:86092470
n3:jazykVysledku
n21:eng
n3:klicovaSlova
social network analysis; sampling; ranking; graphs; graph reduction; complex networks
n3:klicoveSlovo
n12:graph%20reduction n12:complex%20networks n12:graphs n12:social%20network%20analysis n12:sampling n12:ranking
n3:kontrolniKodProRIV
[629733B5EA3B]
n3:mistoKonaniAkce
Beijing
n3:mistoVydani
New York
n3:nazevZdroje
ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
n3:obor
n20:IN
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
4
n3:projekt
n10:ED1.1.00%2F02.0070
n3:rokUplatneniVysledku
n15:2014
n3:tvurceVysledku
Zehnalová, Šárka Horák, Zdeněk Kudělka, Miloš Platoš, Jan
n3:typAkce
n13:WRD
n3:zahajeniAkce
2014-08-17+02:00
s:numberOfPages
6
n8:doi
10.1109/ASONAM.2014.6921688
n22:hasPublisher
Institute of Electrical and Electronics Engineers
n4:isbn
978-1-4799-5877-1
n19:organizacniJednotka
27240