  We investigate hierarchical structure in various complex systems according to
Minimum Spanning Tree methods. Firstly, we investigate stock markets where the
graphis obtained from the matrix of correlations coefficient computed between
all pairs of assets by considering the synchronous time evolution of the
difference of the logarithm of daily stock price. The hierarchical tree
provides information useful to investigate the number and nature of economic
factors that have associated a meaningful economic taxonomy. We continue to use
this method in social systems (sport, political parties and pharmacy) to
investigate collective effects and detect how single element of the system
influences on the other ones. The level of correlations and Minimum Spanning
Trees in various complex systems is also discussed.
