title: Unifying Inference of Meso-Scale Structures in Networks.
abstract: Networks are among the most prevalent formal representations in scientific studies, employed to depict interactions between objects such as molecules, neuronal clusters, or social groups. Studies performed at meso-scale that involve grouping of objects based on their distinctive interaction patterns form one of the main lines of investigation in network science. In a social network, for instance, meso-scale structures can correspond to isolated social groupings or groups of individuals that serve as a communication core. Currently, the research on different meso-scale structures such as community and core-periphery structures has been conducted via independent approaches, which precludes the possibility of an algorithmic design that can handle multiple meso-scale structures and deciding which structure explains the observed data better. In this study, we propose a unified formulation for the algorithmic detection and analysis of different meso-scale structures. This facilitates the investigation of hybrid structures that capture the interplay between multiple meso-scale structures and statistical comparison of competing structures, all of which have been hitherto unavailable. We demonstrate the applicability of the methodology in analyzing the human brain network, by determining the dominant organizational structure (communities) of the brain, as well as its auxiliary characteristics (core-periphery).
authors.affiliation: Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
authors.forename: Birkan
authors.initials: B
authors.lastname: Tunç
authors.affiliation: Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
authors.forename: Ragini
authors.initials: R
authors.lastname: Verma
mesh_terms: D000293:Adolescent; D000465:Algorithms; D001499:Bayes Theorem; D001921:Brain / Q000502:physiology; D001931:Brain Mapping; D056324:Diffusion Tensor Imaging; D006801:Humans; D008297:Male