Module detection in complex networks using integer optimisation

We present a novel solution approach to identify community structure in large complex networks and address resolution limitations in module detection. The proposed algorithm employs modularity to express network community structure and it is based on mixed integer optimisation models. The solution procedure is extended through an iterative procedure to diminish effects that tend to agglomerate smaller modules (resolution limitations).A comprehensive comparative analysis of methodologies for module detection based on modularity maximisation shows that our approach outperforms previously reported methods. Furthermore, in contrast to previous reports, we propose a strategy to handle resolution limitations in modularity maximisation. Overall, we illustrate ways to improve existing methodologies for community structure identification so as to increase its efficiency and applicability.Networks - i.e. groups of entities (nodes or vertices) pairs of which are linked through a form of common property (edges or links) - have formed an efficient representation framework for a variety of complex systems such as social groupings and internet connectivity [1]. The analysis of biological data in systems biology studies through the formalisms of network theory have received particular attention recently, due to the potential benefits that such methodologies can confer in mining the intricate relationships in metabolic networks [2-4], signaling pathways [5], gene regulatory networks [6] or other forms of protein interactions [7]. In general, the abstractions offered by graph theory representations (i) facilitate the analysis of network performance, (ii) provide a unifying framework for comparisons of features across different systems and (iii) assist the mathematical characterisation of system properties and dynamics.Topological properties of networks are particularly important in revealing the organisational principles of nodes within the context of the entire system [8]. Community