getSameCommunityPro​bability(W,nRep)

P = getSameCommunityProbability(W,nRep)

Given an undirected (weighted or binary) connection matrix
with positive and negative weights, the algorithm computes
the community structure nRep times and for each area pair,
computes the a posteriori probability that the 2 areas belong
to the same community. In fact, the community detection algorithm
makes use of heuristics, thus community partition may vary. For this
reason, it is suggested to use nRep > 50

Note: This code builds upon the function 'modularity_louvain_und_sign'
(using the Gómez, Jensen & Arena method implemented in the function),
which is part of the Brain Connectivity Toolbox
(https://sites.google.com/site/bctnet/).


INPUT

W : undirected (weighted or binary) connection matrix
with positive and negative weights

nRep : num. of repetitions of the community detection
algorithm


OUTPUT

P : Probability Matrix (probability that area i & j
were assigned to the same community)



--------------------------------------------------------------------------------------------------------------

- Bettinardi, R. G., Tort-Colet, N., Ruiz-Mejias, M., Sanchez-Vives, M. V., & Deco, G. (2015).
“Gradual emergence of spontaneous correlated brain activity during fading of general anesthesia in rats:
Evidences from fMRI and local field potentials.” Neuroimage, 114, 185-198.
DOI: https://doi.org/10.1016/j.neuroimage.2015.03.037

--------------------------------------------------------------------------------------------------------------