Blockmodelling is the process of determining community structure in a graph. Real graphs contain noise and so it is up to the blockmodelling method to allow for this noise and reconstruct the most likely role memberships and role relationships. Relationships are encoded in a graph using the absence and presence of edges. Two objects are considered similar if they each have edges to a third object. However, the information provided by missing edges is ambiguous and therefore can be measured in different ways. In this article, we examine the effect of the choice of block metric on blockmodelling accuracy and find that data relationships can be position based or set based. We hypothesise that this is due to the data containing either Hamming noise or Jaccard noise. Experiments performed on simulated data show that when no noise is present, the accuracy is independent of the choice of metric. But when noise is introduced, high accuracy results are obtained when the choice of metric matches the type of noise.