Hierarchical representation of socio-economic complex systems according to minimal sapnning trees

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.

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