Knowledge of protein sequences has exploded, but knowledge of protein function is needed to make use of sequence information, and this lags behind. A protein's function must be understood in context and part of this is the network of interactions between proteins. What are the relationships between protein function and the structure of the interaction network? In the first part of my thesis, I investigate the functional relevance of clusters, or communities, of proteins in the yeast protein interaction network. Communities are candidates for biological modules. The work I present is the first to systematically investigate this structure at multiple scales in such networks. I develop novel tests to assess whether communities are functionally homogeneous, and demonstrate that almost every protein is found in a functionally homogeneous community at some scale. The evolution of protein sequences is well-studied, but comparatively little is known about the evolution of protein function. Such knowledge is needed to understand when it is appropriate to annotate newly sequenced proteins by transferring functional information from homologs-i.e. evolutionarily related proteins. In the second part of my thesis, I assess the success of transferring protein-protein interactions across species and use this to estimate the rate at which interactions are lost in evolution. At levels of sequence similarity associated with functional annotation transfer, I demonstrate that protein-protein interaction transfer is unreliable. The relevance of community structure for understanding protein function and the low conservation of individual interactions, suggests a possible role for communities in the evolution of cellular function. I discuss this possibility in my conclusions.