ǂa ǂMonte Carlo simulation study
Marjan Cugmas (Avtor), Aleš Žiberna (Avtor)

Povzetek

Blockmodeling refers to a variety of statistical methods for reducing and simplifying large and complex networks. While methods for blockmodeling networks observed at one time point are well established, it is only recently that researchers have proposed several methods for analysing dynamic networks (i.e., networks observed at multiple time points). The considered approaches are based on k-means or stochastic blockmodeling, with different ways being used to model time dependency among time points. Their novelty means they have yet to be extensively compared and evaluated and the paper therefore aims to compare and evaluate them using Monte Carlo simulations. Different network characteristics are considered, including whether tie formation is random or governed by local network mechanisms. The results show the Dynamic Stochastic Blockmodel (Matias and Miele 2017) performs best if the blockmodel does not change; otherwise, the Stochastic Blockmodel for Multipartite Networks (Bar-Hen et al. 2020) does.

Ključne besede

dynamic networks;stochastic blockmodeling;K-means blockmodeling;simulations;local mechanisms;evaluation;

Podatki

Jezik: Angleški jezik
Leto izida:
Tipologija: 1.01 - Izvirni znanstveni članek
Organizacija: UL FDV - Fakulteta za družbene vede
UDK: 303
COBISS: 134684931 Povezava se bo odprla v novem oknu
ISSN: 0378-8733
Št. ogledov: 48
Št. prenosov: 29
Ocena: 0 (0 glasov)
Metapodatki: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Ostali podatki

Sekundarni jezik: Slovenski jezik
Sekundarne ključne besede: Družbena omrežja;Analiza omrežij (družbene vede);Bločno modeliranje;
Vrsta dela (COBISS): Članek v reviji
Strani: str. 7-19
Zvezek: ǂVol. ǂ73
Čas izdaje: May 2023
DOI: 10.1016/j.socnet.2022.12.003
ID: 17469773