Hierarchical Overlapping Community Detection for Weighted Networks

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

Hierarchical Overlapping Community Detection for Weighted Networks

Original language description

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.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

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

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2023

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

"159–171"

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

001264437200013