One of the central questions that a similarity measure for software decompositions has to address is whether to consider discrepancies in terms of the nodes of a particular decomposition, or assess similarity based on differences in clustering the edges of the system's dependency graph.

In this paper, we argue that considering nodes or edges in isolation is too one-sided. We outline shortcomings of previous approaches, and introduce the first dissimilarity measure that takes both nodes and edges into account. We also present experiments on real and synthetic data sets that illustrate the differences between various measures.