title: Small-World Propensity and Weighted Brain Networks.
abstract: Quantitative descriptions of network structure can provide fundamental insights into the function of interconnected complex systems. Small-world structure, diagnosed by high local clustering yet short average path length between any two nodes, promotes information flow in coupled systems, a key function that can differ across conditions or between groups. However, current techniques to quantify small-worldness are density dependent and neglect important features such as the strength of network connections, limiting their application in real-world systems. Here, we address both limitations with a novel metric called the Small-World Propensity (SWP). In its binary instantiation, the SWP provides an unbiased assessment of small-world structure in networks of varying densities. We extend this concept to the case of weighted brain networks by developing (i) a standardized procedure for generating weighted small-world networks, (ii) a weighted extension of the SWP, and (iii) a method for mapping observed brain network data onto the theoretical model. In applying these techniques to compare real-world brain networks, we uncover the surprising fact that the canonical biological small-world network, the C. elegans neuronal network, has strikingly low SWP. These metrics, models, and maps form a coherent toolbox for the assessment and comparison of architectural properties in brain networks.
authors.affiliation: Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104 USA.|US Army Research Laboratory, Aberdeen Proving Ground, MD 21005, USA.|Department of Mathematics and Computational and Data-Enabled Science and Engineering Program, University at Buffalo, Buffalo, NY 14260 USA.
authors.forename: Sarah Feldt
authors.initials: SF
authors.lastname: Muldoon
authors.affiliation: Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104 USA.|Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218 USA.
authors.forename: Eric W
authors.initials: EW
authors.lastname: Bridgeford
authors.affiliation: Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104 USA.|Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104 USA.
authors.forename: Danielle S
authors.initials: DS
authors.lastname: Bassett
mesh_terms: D000465:Algorithms; D000818:Animals; D001921:Brain / Q000502:physiology*; D016000:Cluster Analysis; D006801:Humans; D008954:Models, Biological; D016571:Neural Networks, Computer