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. •Linked networks contain two or more sets of units and subnetworks.•Subnetworks contain ties among the units of one set or between units of two sets.•Examples of linked networks are also dynamic networks and multilevel networks.•Blockmodeling linked networks jointly partitions all sets of units.•A stochastic blockmodeling approach is utilized to blockmodeling linked networks.•Weighted likelihood is used to balance the impact of different subnetworks.