Topic Extension Using the Network Extracted from DBLP

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F47813059%3A19520%2F13%3A%230002440" target="_blank" >RIV/47813059:19520/13:#0002440 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27240/13:86086965

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Topic Extension Using the Network Extracted from DBLP

Popis výsledku v původním jazyce

This article focuses on the topic extension in an area that is initially specified by the user through the topic's keywords. The extended area of interest defined by keywords is determined by a set of terms used by the community for which the selected keywords are significant. The extracted topic by selected communities can be used to update and broaden the area of interest. This new evaluation of edges depends on terms that appear in the titles of articles of two co-Authors. The newly evaluated networkmore accurately describes the intensity of the relationships between co-Authors. This network is suitable as an input to models, which are focused on prediction of future relationships and community structures in co-Author networks. Moreover, the topicextension may be used in prediction models for the extraction of expected keywords which will be used in a given community.

Název v anglickém jazyce

Topic Extension Using the Network Extracted from DBLP

Popis výsledku anglicky

This article focuses on the topic extension in an area that is initially specified by the user through the topic's keywords. The extended area of interest defined by keywords is determined by a set of terms used by the community for which the selected keywords are significant. The extracted topic by selected communities can be used to update and broaden the area of interest. This new evaluation of edges depends on terms that appear in the titles of articles of two co-Authors. The newly evaluated networkmore accurately describes the intensity of the relationships between co-Authors. This network is suitable as an input to models, which are focused on prediction of future relationships and community structures in co-Author networks. Moreover, the topicextension may be used in prediction models for the extraction of expected keywords which will be used in a given community.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2013

Kód důvěrnosti údajů

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

Název statě ve sborníku

25th European Modeling and Simulation Symposium, EMSS 2013

ISBN

978-88-97999-22-5

ISSN

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e-ISSN

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Počet stran výsledku

7

Strana od-do

410-417

Název nakladatele

Elsevier

Místo vydání

Brusel

Místo konání akce

Athens, Greece

Datum konání akce

25. 9. 2013

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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