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

Communities of dense weighted networks: MicroRNA co-target network as an example