Graph construction based on local representativeness

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F17%3A10238662" target="_blank" >RIV/61989100:27240/17:10238662 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-319-62389-4_54" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-319-62389-4_54</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-62389-4_54" target="_blank" >10.1007/978-3-319-62389-4_54</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Graph construction based on local representativeness

Popis výsledku v původním jazyce

Graph construction is a known method of transferring the problem of classic vector data mining to network analysis. The advantage of networks is that the data are extended by links between certain (similar) pairs of data objects, so relationships in the data can then be visualized in a natural way. In this area, there are many algorithms, often with significantly different results. A common problem for all algorithms is to find relationships in data so as to preserve the characteristics related to the internal structure of the data. We present a method of graph construction based on a network reduction algorithm, which is found on analysis of the representativeness of the nodes of the network. It was verified experimentally that this algorithm preserves structural characteristics of the network during the reduction. This approach serves as the basis for our method which does not require any default parameters. In our experiments, we show the comparison of our graph construction method with one well-known method based on the most commonly used approach. © 2017, Springer International Publishing AG.

Název v anglickém jazyce

Graph construction based on local representativeness

Popis výsledku anglicky

Graph construction is a known method of transferring the problem of classic vector data mining to network analysis. The advantage of networks is that the data are extended by links between certain (similar) pairs of data objects, so relationships in the data can then be visualized in a natural way. In this area, there are many algorithms, often with significantly different results. A common problem for all algorithms is to find relationships in data so as to preserve the characteristics related to the internal structure of the data. We present a method of graph construction based on a network reduction algorithm, which is found on analysis of the representativeness of the nodes of the network. It was verified experimentally that this algorithm preserves structural characteristics of the network during the reduction. This approach serves as the basis for our method which does not require any default parameters. In our experiments, we show the comparison of our graph construction method with one well-known method based on the most commonly used approach. © 2017, Springer International Publishing AG.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

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

Projekt

<a href="/cs/project/NV16-31852A" target="_blank" >NV16-31852A: Predikce rizika reoperace u pacientů s TEP kyčlí a kolen na základě imunogenetického vyšetření: vývoj kalkulátoru rizika pro rutinní klinické použití</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Volume 10392

ISBN

978-3-319-62388-7

ISSN

0302-9743

e-ISSN

1611-3349

Počet stran výsledku

12

Strana od-do

654-665

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Hongkong

Datum konání akce

3. 8. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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