Before revision:  We find that the sparsification method is highly sensitive to deletion of edges leading to significant inconsistencies with respect to the loss of information and that graph spectral methods were the most irregular measure only capturing algebraic information in a condensed fashion but in that process largely lost the information content of the original networks.
After revision:  We find that the sparsification method is highly sensitive to high number of edge deletion, leading to significant inconsistencies with respect to the loss of information and that graph spectral methods were the most irregular measure only capturing algebraic information in a condensed fashion but in that process largely lost the information content of the original networks.