NDLI: Modularity gradients: Measuring the contribution of edges to the community structure of a brain network

Content Provider	IEEE Xplore Digital Library
Author	Yu-Teng Chang Pantazis, D.
Copyright Year	2013
Description	Author affiliation: McGovern Inst. for Brain Res., Massachusetts Inst. of Technol., Cambridge, MA, USA (Yu-Teng Chang; Pantazis, D.)
Abstract	Modularity measures the quality of a particular division of a brain network, and it tends to have high values for networks with strong community structure. In this paper we show that the derivative of modularity with respect to each edge in a network quantifies the contribution of the edge to the global modular structure of the network. We derive analytical forms for the derivative of modularity and investigate its properties. Our approach focuses on the significance of edges on modularity and deviates from standard node-centric network measures, such as hub analysis.
Sponsorship	IEEE
Starting Page	536
Ending Page	539
File Size	1655543
Page Count	4
File Format	PDF
ISBN	9781467364560
ISSN	19457928
e-ISBN	9781467364553
DOI	10.1109/ISBI.2013.6556530
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-04-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Communities Biomedical measurement Image edge detection Brain modeling Indexes Autism Atmospheric measurements brain connectome modularity community structure edge gradient edge significance
Content Type	Text
Resource Type	Article

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Modularity gradients: Measuring the contribution of edges to the community structure of a brain network