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September 2019 Network classification with applications to brain connectomics
Jesús D. Arroyo Relión, Daniel Kessler, Elizaveta Levina, Stephan F. Taylor
Ann. Appl. Stat. 13(3): 1648-1677 (September 2019). DOI: 10.1214/19-AOAS1252


While statistical analysis of a single network has received a lot of attention in recent years, with a focus on social networks, analysis of a sample of networks presents its own challenges which require a different set of analytic tools. Here we study the problem of classification of networks with labeled nodes, motivated by applications in neuroimaging. Brain networks are constructed from imaging data to represent functional connectivity between regions of the brain, and previous work has shown the potential of such networks to distinguish between various brain disorders, giving rise to a network classification problem. Existing approaches tend to either treat all edge weights as a long vector, ignoring the network structure, or focus on graph topology as represented by summary measures while ignoring the edge weights. Our goal is to design a classification method that uses both the individual edge information and the network structure of the data in a computationally efficient way, and that can produce a parsimonious and interpretable representation of differences in brain connectivity patterns between classes. We propose a graph classification method that uses edge weights as predictors but incorporates the network nature of the data via penalties that promote sparsity in the number of nodes, in addition to the usual sparsity penalties that encourage selection of edges. We implement the method via efficient convex optimization and provide a detailed analysis of data from two fMRI studies of schizophrenia.


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Jesús D. Arroyo Relión. Daniel Kessler. Elizaveta Levina. Stephan F. Taylor. "Network classification with applications to brain connectomics." Ann. Appl. Stat. 13 (3) 1648 - 1677, September 2019.


Received: 1 January 2017; Revised: 1 January 2019; Published: September 2019
First available in Project Euclid: 17 October 2019

zbMATH: 07145971
MathSciNet: MR4019153
Digital Object Identifier: 10.1214/19-AOAS1252

Keywords: fMRI data , Graph classification , High-dimensional data , Variable selection

Rights: Copyright © 2019 Institute of Mathematical Statistics


Vol.13 • No. 3 • September 2019
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