  Complex networks are intrinsically modular. Resolving small modules is
particularly difficult when the network is densely connected; wide variation of
link weights invites additional complexities. In this article we present an
algorithm to detect community structure in densely connected weighted networks.
First, modularity of the network is calculated by erasing the links having
weights smaller than a cutoff $q.$ Then one takes all the disjoint components
obtained at $q=q_c,$ where the modularity is maximum, and modularize the
components individually using Newman Girvan's algorithm for weighted networks.
We show, taking microRNA (miRNA) co-target network of Homo sapiens as an
example, that this algorithm could reveal miRNA modules which are known to be
relevant in biological context.
