  Bipartite networks are a useful tool for representing and investigating
interaction networks. We consider methods for identifying communities in
bipartite networks. Intuitive notions of network community groups are made
explicit using Newman's modularity measure. A specialized version of the
modularity, adapted to be appropriate for bipartite networks, is presented; a
corresponding algorithm is described for identifying community groups through
maximizing this measure. The algorithm is applied to networks derived from the
EU Framework Programs on Research and Technological Development. Community
groups identified are compared using information-theoretic methods.
