20.500.12556/RUL-135305 Stochastic blockmodeling of linked networks Blockmodeling linked networks aims to simultaneously cluster two or more sets of units into clusters based on a network where ties are possible both between units from the same set as well as between units of different sets. While this has already been developed for generalized and k-means blockmodeling, our approach is based on the well-known stochastic blockmodeling technique, utilizing a mixture model. Estimation is performed using the CEM algorithm, which iteratively estimates the parameters by maximizing a suitable likelihood function and reclusters the units according to the parameters. The steps are repeated until the likelihood function ceases to improve. A key drawback of the basic algorithm is that it treats all units equally, consequently yielding higher influence to larger parts of the data. The greater size, however, does not necessarily imply higher importance. To mitigate this asymmetry, we propose a solution where underrepresented parts of the data are given more influence through an appropriate weighting. This idea leads to the so-called weighted likelihood approach, where the ordinary likelihood function is replaced by a weighted likelihood. The efficiency of the different approaches is tested via simulations. It is shown through simulations that the weighted likelihood approach performs better for larger networks and a clearer blockmodel structure, especially when the one-mode blockmodels within the smaller sets are clearer. stochastic blockmodeling linked network weighted likelihood CEM algorithm mixture model stohastično bločno modeliranje analiza omrežij družbene vede bločno modeliranje true false true Angleški jezik Slovenski jezik Članek v reviji 2022-03-07 13:36:40 2022-03-07 13:36:43 2022-09-14 04:12:41 0000-00-00 00:00:00 2022 0 0 Str. 240-252 Vol. 70 Jul. 2022 0000-00-00 Zaloznikova Objavljeno apc 0000-00-00 0000-00-00 0000-00-00 303:004.42 0378-8733 10.1016/j.socnet.2022.02.001 98506755 1-s2.0-S037887332200017X-main.pdf 1-s2.0-S037887332200017X-main.pdf 1 3C5488D10261A12F0A20962E135180EC fe982505f18088bd03f5e98760501c75067cca266fba353975b7accc1888b130 059d167c-29c0-11ed-92af-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=159912 https://www.sciencedirect.com/science/article/pii/S037887332200017X 0 c04f3bff-29bf-11ed-92af-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=159911 Fakulteta za družbene vede 0 0 0