Approaches to blockmodeling dynamic networks

ǂa ǂMonte Carlo simulation study

Marjan Cugmas (Author), Aleš Žiberna (Author)

Abstract

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.

Keywords

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

Data

Language:	English
Year of publishing:	2023
Typology:	1.01 - Original Scientific Article
Organization:	UL FDV - Faculty of Social Sciences
UDC:	303
COBISS:	134684931
ISSN:	0378-8733
Views:	48
Downloads:	29
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	Slovenian
Secondary keywords:	Družbena omrežja;Analiza omrežij (družbene vede);Bločno modeliranje;
Type (COBISS):	Article
Pages:	str. 7-19
Issue:	ǂVol. ǂ73
Chronology:	May 2023
DOI:	10.1016/j.socnet.2022.12.003
ID:	17469773