Hierarchical Overlapping Community Detection for Weighted Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10256316" target="_blank" >RIV/61989100:27240/23:10256316 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-031-53499-7_13" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-53499-7_13</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-53499-7_13" target="_blank" >10.1007/978-3-031-53499-7_13</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Hierarchical Overlapping Community Detection for Weighted Networks

Popis výsledku v původním jazyce

Real-world networks often contain community structures, where nodes form tightly interconnected clusters. Recent research indicates hierarchical organization, where vertices split into groups that further subdivide across multiple scales. However, individuals in social networks typically belong to multiple communities due to their various affiliations, such as family, friends, and colleagues. These overlaps will emerge in the community structure of online social networks and other complex networks like in biology, where nodes have diverse functions. In this work, we propose an algorithm for hierarchical overlapping community detection in weighted networks. The overlap between clusters is realized via maximal cliques that are used as base elements for hierarchical agglomerative clustering on the graph (GHAC). The closed trail distance and the size of the maximal clique in overlap are used for the dissimilarity between clusters in agglomerative steps of the GHAC. The closed trail distance is designed for weighted networks.Experiments on synthetic networks and different evaluations of the results of experiments show that the proposed algorithm is comparable with other widely used algorithms for overlapping community detection and is efficient for detecting hierarchy structure in weighted networks.

Název v anglickém jazyce

Hierarchical Overlapping Community Detection for Weighted Networks

Popis výsledku anglicky

Real-world networks often contain community structures, where nodes form tightly interconnected clusters. Recent research indicates hierarchical organization, where vertices split into groups that further subdivide across multiple scales. However, individuals in social networks typically belong to multiple communities due to their various affiliations, such as family, friends, and colleagues. These overlaps will emerge in the community structure of online social networks and other complex networks like in biology, where nodes have diverse functions. In this work, we propose an algorithm for hierarchical overlapping community detection in weighted networks. The overlap between clusters is realized via maximal cliques that are used as base elements for hierarchical agglomerative clustering on the graph (GHAC). The closed trail distance and the size of the maximal clique in overlap are used for the dissimilarity between clusters in agglomerative steps of the GHAC. The closed trail distance is designed for weighted networks.Experiments on synthetic networks and different evaluations of the results of experiments show that the proposed algorithm is comparable with other widely used algorithms for overlapping community detection and is efficient for detecting hierarchy structure in weighted networks.

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

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

Studies in Computational Intelligence. Volume 1142

ISBN

978-3-031-53498-0

ISSN

1860-949X

e-ISSN

1860-9503

Počet stran výsledku

12

Strana od-do

"159–171"

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Menton

Datum konání akce

28. 11. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001264437200013