Protein interaction networks are a promising type of data for studying
complex biological systems. However, despite the rich information embedded in
these networks, they face important data quality challenges of noise and
incompleteness that adversely affect the results obtained from their analysis.
Here, we explore the use of the concept of common neighborhood similarity
(CNS), which is a form of local structure in networks, to address these issues.
Although several CNS measures have been proposed in the literature, an
understanding of their relative efficacies for the analysis of interaction
networks has been lacking. We follow the framework of graph transformation to
convert the given interaction network into a transformed network corresponding
to a variety of CNS measures evaluated. The effectiveness of each measure is
then estimated by comparing the quality of protein function predictions
obtained from its corresponding transformed network with those from the
original network. Using a large set of S. cerevisiae interactions, and a set of
136 GO terms, we find that several of the transformed networks produce more
accurate predictions than those obtained from the original network. In
particular, the HC.cont measure proposed here performs particularly well for
this task. Further investigation reveals that the two major factors
contributing to this improvement are the abilities of CNS measures, especially
HC.cont, to prune out noisy edges and introduce new links between
functionally related proteins
