[1] While the generalized blockmodeling signifies a "formal and integrated approach for the study of the underlying functional anatomies of virtually any set of relational data", it is in principle used for binary networks.
This is evident from the set of ideal blocks, which are used to interpret blockmodels, that are binary, based on the characteristic link patterns.
With this, "an optional parameter determines the prominence of valued ties as a minimum percentile deviation between observed and expected flows".
Such maximum two–sided deviation threshold, holding the aggregate uncertainty score at zero or near–zero levels, is then proposed as "a measure of interpretational certainty for valued blockmodels, in effect transforming the optional parameter into an outgoing state".
[2] With this approach, more information is retained for analysis, which also means, that there are fewer partitions having identical values of the criterion function.