  This paper investigates community detection by modularity maximisation on
bipartite networks. In particular we are interested in how the operation of
projection, using one node set of the bipartite network to infer connections
between nodes in the other set, interacts with community detection. We first
define a notion of modularity appropriate for a projected bipartite network and
outline an algorithm for maximising it in order to partition the network. Using
both real and synthetic networks we compare the communities found by five
different algorithms, where each algorithm maximises a different modularity
function and sees different aspects of the bipartite structure. Based on these
results we suggest a simple heuristic for finding communities in bipartite
networks.
