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Journal of Classification

, Volume 32, Issue 1, pp 3–20 | Cite as

Shuffled Graph Classification: Theory and Connectome Applications

  • Joshua T. VogelsteinEmail author
  • Carey E. Priebe
Article

Abstract

We develop a formalism to address statistical pattern recognition of graph valued data. Of particular interest is the case of all graphs having the same number of uniquely labeled vertices. When the vertex labels are latent, such graphs are called shuffled graphs. Our formalism provides insight to trivially answer a number of open statistical questions including: (i) under what conditions does shuffling the vertices degrade classification performance and (ii) do universally consistent graph classifiers exist? The answers to these questions lead to practical heuristic algorithms with state-of-the-art finite sample performance, in agreement with our theoretical asymptotics. Applying these methods to classify sex and autism in two different human connectome classification tasks yields successful classification results in both applications.

Keywords

Statistical pattern recognition Random graphs Graph matching Connectomics 

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Copyright information

© Classification Society of North America 2015

Authors and Affiliations

  1. 1.Johns Hopkins UniversityBaltimoreUSA
  2. 2.Duke UniversityDurhamUSA
  3. 3.Child Mind InstituteNew YorkUSA
  4. 4.DurhamUSA

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