Fitting a generative model to data makes a statement about the mechanisms that generated a network. Thus, in so far as the model is a good description of the data, it is possible to make generalisations and predictions about what has not been observed. This means that it is possible to use SBMs to determine the probability of a non-observed edge to be missing from the network . This can be particularly useful if a given network observation is incomplete, or contains errors or noise.