Since correlation may be interpreted as a measure of the influence across time‐series, it may be conveniently mapped into a distance and into a weighted adjacency matrix. Based on such matrix, network theory has attempted to filter out the noise in correlation matrices by extracting the dominant hierarchy (i.e. the strongest linear‐dependence signals) within time‐series.
