  Uncovering latent community structure in complex networks is a field that has
received an enormous amount of attention. Unfortunately, whilst potentially
very powerful, unsupervised methods for uncovering labels based on topology
alone has been shown to suffer from several difficulties. For example, the
search space for many module extraction approaches, such as the modularity
maximisation algorithm, appears to be extremely glassy, with many high valued
solutions that lack any real similarity to one another. However, in this paper
we argue that this is not a flaw with the modularity maximisation algorithm
but, rather, information that can be used to aid the context specific
classification of functional relationships between vertices. Formally, we
present an approach for generating a high value modularity consensus space for
a network, based on the ensemble space of locally optimal modular partitions.
We then use this approach to uncover latent relationships, given small query
sets. The methods developed in this paper are applied to biological and social
datasets with ground-truth label data, using a small number of examples used as
seed sets to uncover relationships. When tested on both real and synthetic
datasets our method is shown to achieve high levels of classification accuracy
in a context specific manner, with results comparable to random walk with
restart methods.
