Before revision:  We employ methods that allow us to identify communities in the network at multiple resolutions as, a priori, there is no single scale of interest. We use novel, stringent tests to find strong evidence for a link between topology and function. Crucially, our tests control for the fact that interacting partners are more similar than a randomly-chosen pair, which is essential for a fair test of functional similarity. We find that many biologically homogeneous communities , that are robust over many resolutions, are surprisingly large. We thus not only identify complexes from the interaction data but also larger collections of strongly-interacting proteins . Communities that contain interactions from tandem affinity purification and co-immunoprecipitation data are far more likely to be biologically homogeneous than those from yeast-two-hybrid and split-ubiquitin data.
After revision:  We employ methods that allow us to identify communities in the communities that we find depends on the scales at which we probe the network. Results: We find many proteins lie in functionally homogeneous communities (a maximum of 2777 out of 4028 proteins) which suggests that network structure does indeed help identify sets of proteins with similar functions. The homogeneity of the communities depends on the scale selected. We use a novel test and two independent characterizations of protein function to determine the functional homogeneity of communities. We exploit the connection between network structure and split-ubiquitin data.