Enhanced Detectability of Community Structure in Multilayer Networks through Layer Aggregation
Year: 2016
Type: article
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(L^{-1/2}) 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.
Source: Physical Review Letters
Institution: University of North Carolina at Chapel Hill
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
Funders: James S. McDonnell Foundation, National Institutes of Health, National Cancer Institute, National Institute of General Medical Sciences, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Eunice Kennedy Shriver National Institute of Child Health and Human Development
OpenAlex ID: W2252566672