This folder contains the supplemental materials for 
"Bridge Centrality: A Network Approach to Understanding Comorbidity".

MAIN FOLDER CONTENTS:
Code.R -- A file containing R code used for the analysis
Data_for_tutorial.csv -- A CSV file containing example data
 for the tutorial
Networks -- We used data from a variety of sources, not all 
 of which could be shared publicly. Thus, we instead include
 the adjacency matrices for each network in this subfolder.
Preprint.pdf -- A preprint version of the manuscript
Tutorial on bridge centrality in R.R -- An R file containing
 a follow-along tutorial explaining how to calculate bridge
 centrality metrics using the networktools R package
Study 3 Results -- In Study 3 of the paper, we reanalyzed
 18 empirical datasets from the literature. The full results
 of this analysis are contained in this subfolder. We present
 two types of data visualization: plots of each network, with
 bridge nodes highlighted, and traditional "centrality plots"
 of each type of bridge centrality.


SUBFOLDER 1 - STUDY 3 RESULTS:
Bridge centrality estimates -- This subfolder contains
 "centrality plots" for each type of bridge centrality
 in each of the 18 reanalyzed networks
Plots of networks with bridges colored -- This 
 subfolder contains network plots of each of 18 
 reanalyzed networks. Bridge nodes are highlighted.
Combined PDF of Study 3 Results -- For convenience, 
 all of the plots in the other two folders have been
 combined into a single PDF with a Table of Contents
 labelling each network by the page of the PDF in which
 it appears

SUBFOLDER 2 - NETWORKS:
Covariance matrices -- When possible, we generated covariance
 matrices based on the data structure. Other network types
 (e.g., GLASSO) can typically be estimated from the covariance 
 matrix. Covariance matrices included as CSVs in this subfolder. Please 
 note that only 1 covariance matrix is computed per dataset,
 even when multiple networks were reported based on that 1 dataset.
Weighted adjacency networks -- When covariance matrices were
 not accessible (e.g., when we only had the network data), we
 created CSVs with the weighted adjacency matrix from the network