Enhanced Detectability of Community Structure in Multilayer Networks through Layer Aggregation

Dane Taylor, Saray Shai, Natalie Stanley, and Peter J. Mucha
Phys. Rev. Lett. 116, 228301 – Published 2 June 2016
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Abstract

Many systems are naturally represented by a multilayer network in which edges exist in multiple layers that encode different, but potentially related, types of interactions, and it is important to understand limitations on the detectability of community structure in these networks. Using random matrix theory, we analyze detectability limitations for multilayer (specifically, multiplex) stochastic block models (SBMs) in which L layers are derived from a common SBM. We study the effect of layer aggregation on detectability for several aggregation methods, including summation of the layers’ adjacency matrices for which we show the detectability limit vanishes as O(L1/2) with increasing number of layers, L. Importantly, we find a similar scaling behavior when the summation is thresholded at an optimal value, providing insight into the common—but not well understood—practice of thresholding pairwise-interaction data to obtain sparse network representations.

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  • Received 16 November 2015

DOI:https://doi.org/10.1103/PhysRevLett.116.228301

© 2016 American Physical Society

Physics Subject Headings (PhySH)

Networks

Authors & Affiliations

Dane Taylor1,*, Saray Shai1, Natalie Stanley1,2, and Peter J. Mucha1

  • 1Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill, North Carolina 27599, USA
  • 2Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, North Carolina 27599, USA

  • *dane.r.taylor@gmail.com

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Issue

Vol. 116, Iss. 22 — 3 June 2016

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