title: Resolution limit in community detection.
abstract: Detecting community structure is fundamental for uncovering the links between structure and function in complex networks and for practical applications in many disciplines such as biology and sociology. A popular method now widely used relies on the optimization of a quantity called modularity, which is a quality index for a partition of a network into communities. We find that modularity optimization may fail to identify modules smaller than a scale which depends on the total size of the network and on the degree of interconnectedness of the modules, even in cases where modules are unambiguously defined. This finding is confirmed through several examples, both in artificial and in real social, biological, and technological networks, where we show that modularity optimization indeed does not resolve a large number of modules. A check of the modules obtained through modularity optimization is thus necessary, and we provide here key elements for the assessment of the reliability of this community detection method.
authors.affiliation: School of Informatics and Center for Biocomplexity, Indiana University, Bloomington, IN 47406, USA.
authors.forename: Santo
authors.initials: S
authors.lastname: Fortunato
authors.forename: Marc
authors.initials: M
authors.lastname: Barthélemy
mesh_terms: D000818:Animals; D019058:Community Networks; D006801:Humans; D008962:Models, Theoretical