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

Abstract

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.