A new criterion function for exploratory blockmodeling for structural and regular equivalence

We present a new criterion function for blockmodeling two-way two-mode relation matrices when the number of blocks as well as the equivalence relation are unknown. For this, we specify a measure of fit based on data compression theory, which allows for the comparison of blockmodels of different sizes and block types from different equivalence relations. We arm an alternating optimization algorithm with this criterion and demonstrate that the method reproduces consensual blockings of three datasets without any pre-specification. We perform a simulation study where we compare our compression-based criterion to the commonly used criterion that measures the number of inconsistencies with an ideal blockmodel.

► Method for blockmodeling two-mode relation matrices with unknown number of clusters.
► Criterion function forgoes the need to pre-specify block types and their locations.
► Efficient alternating optimization algorithm.