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• W2411622228 abstract "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." @default.
• W2411622228 created "2016-06-24" @default.
• W2411622228 creator A5009266404 @default.
• W2411622228 creator A5015567863 @default.
• W2411622228 creator A5085717731 @default.
• W2411622228 date "2016-02-25" @default.
• W2411622228 modified "2023-10-17" @default.
• W2411622228 title "Small-World Propensity and Weighted Brain Networks" @default.
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• W2411622228 doi "https://doi.org/10.1038/srep22057" @default.
• W2411622228 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4766852" @default.
• W2411622228 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/26912196" @default.
• W2411622228 hasPublicationYear "2016" @default.
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