title: Improving the performance of algorithms to find communities in networks.
abstract: Most algorithms to detect communities in networks typically work without any information on the cluster structure to be found, as one has no a priori knowledge of it, in general. Not surprisingly, knowing some features of the unknown partition could help its identification, yielding an improvement of the performance of the method. Here we show that, if the number of clusters was known beforehand, standard methods, like modularity optimization, would considerably gain in accuracy, mitigating the severe resolution bias that undermines the reliability of the results of the original unconstrained version. The number of clusters can be inferred from the spectra of the recently introduced nonbacktracking and flow matrices, even in benchmark graphs with realistic community structure. The limit of such a two-step procedure is the overhead of the computation of the spectra.
authors.affiliation: Department of Biomedical Engineering and Computational Science, Aalto University School of Science, P.O. Box 12200, FI-00076, Finland.
authors.forename: Richard K
authors.initials: RK
authors.lastname: Darst
authors.affiliation: Physics Department, Washington University in St. Louis, CB 1105, One Brookings Drive, St. Louis, Missouri 63130-4899, USA.
authors.forename: Zohar
authors.initials: Z
authors.lastname: Nussinov
authors.affiliation: Department of Biomedical Engineering and Computational Science, Aalto University School of Science, P.O. Box 12200, FI-00076, Finland.
authors.forename: Santo
authors.initials: S
authors.lastname: Fortunato
mesh_terms: D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical