  Despite the recent advances in developing more effective thresholding methods
to convert weighted networks to unweighted counterparts, there are still
several limitations that need to be addressed. One such limitation is the
inability of the most existing thresholding methods to take into account the
topological properties of the original weighted networks during the
binarization process, which could ultimately result in unweighted networks that
have drastically different topological properties than the original weighted
networks. In this study, we propose a new thresholding method based on the
percolation theory to address this limitation. The performance of the proposed
method was validated and compared to the existing thresholding methods using
simulated and real-world functional connectivity networks in the brain.
Comparison of macroscopic and microscopic properties of the resulted unweighted
networks to the original weighted networks suggest that the proposed
thresholding method can successfully maintain the topological properties of the
original weighted networks.
