20.500.12556/RUL-143411 Approaches to blockmodeling dynamic networks a Monte Carlo simulation study 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. dynamic networks stochastic blockmodeling k-means blockmodeling simulations local mechanisms evaluation družbena omrežja analiza omrežij družbene vede bločno modeliranje true false true Angleški jezik Slovenski jezik Članek v reviji 2022-12-20 10:13:49 2022-12-20 10:13:52 2023-01-24 03:36:05 0000-00-00 00:00:00 2023 0 0 Str. 7-19 Vol. 73 May 2023 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 303 0378-8733 10.1016/j.socnet.2022.12.003 134684931 Cugmas_Ziberna_SN.pdf Cugmas_Ziberna_SN.pdf 1 B06937544F95AC5ED3765AF8DE446194 196800ad843b675d91c07070b78c9acbf52a0cb2d3070d1b5164b6d1c553e162 eaf39039-8046-11ed-9c94-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=165071 https://www.sciencedirect.com/science/article/pii/S0378873322001022 0 f6bd9951-8046-11ed-9c94-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=165072 Fakulteta za družbene vede 0 0 0