  We demonstrate how analysis of co-clustering in bipartite networks may be
used as a bridge to connect, compare and complement clustering results about
community structure in two different spaces: single-mode bipartite network
projections. As a case study we consider scientific knowledge, which is
represented as a complex bipartite network of articles and related concepts.
Connecting clusters of articles and clusters of concepts via article-to-concept
bipartite co-clustering, we demonstrate how concept features (e.g. subject
classes) may be inferred from the article ones.
