Detecting Strong Cliques in Co-authorship Networks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10257003" target="_blank" >RIV/61989100:27240/24:10257003 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/chapter/10.1007/978-3-031-53499-7_16" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-53499-7_16</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-53499-7_16" target="_blank" >10.1007/978-3-031-53499-7_16</a>

Result language

angličtina

Original language name

Detecting Strong Cliques in Co-authorship Networks

Original language description

The study of complete sub-graphs belongs to the classical problems of graph theory. Thanks to sociology, the term clique has come to be used for structures representing a small group of people or other entities who share common characteristics and know each other. Clique detection algorithms can be applied in all domains where networks are used to describe relationships among entities. That is not only in social, information, or communication networks but also in biology, chemistry, medicine, etc. In large-scale, e.g., social networks, cliques can have hundreds or more nodes. On the other hand, e.g., in co-authorship networks representing publishing activities of groups of authors, cliques contain, at most, low dozens of nodes.Our paper describes experiments on detecting strong cliques in two weighted co-authorship networks. These experiments are motivated by the assumption that not every clique detected by traditional algorithms truly satisfies the sociological assumption above. Informally speaking, the approach presented in this paper assumes that each pair of clique nodes must be closer to each other and other clique nodes than to non-clique nodes. Using experiments with weighted co-authorship networks, we show how clique detection results differ from the traditional approach when both the strength of the edge (weight) and the structural neighborhood of the clique are considered simultaneously in the analysis.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10200 - Computer and information sciences

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2024

Confidentiality

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

Article name in the collection

Studies in Computational Intelligence. Volume 1142

ISBN

978-3-031-53498-0

ISSN

1860-949X

e-ISSN

1860-9503

Number of pages

12

Pages from-to

197-208

Publisher name

Springer

Place of publication

Cham

Event location

Menton

Event date

Nov 28, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001264437200016