  We review the recent approach of correlation based networks of financial
equities. We investigate portfolio of stocks at different time horizons,
financial indices and volatility time series and we show that meaningful
economic information can be extracted from noise dressed correlation matrices.
We show that the method can be used to falsify widespread market models by
directly comparing the topological properties of networks of real and
artificial markets.
