In the analysis of multivariate time series, the first objective is the estimation of the connectivity structure of the observed variables (or subsystems), where connectivity is also referred to as inter-dependence, coupling, information flow or Granger causality. Depending on the type of analysis one wants to pursues, also indicated by the size of the data, one selects a connectivity measure to estimate the driving-response connections among the observed variables. For example, if the multi-variate time series is very short, one would rather use a linear measure of bivariate (Granger) causality, or even the linear cross-correlation. On the other extreme of a very long multivariate time series, one would prefer to use a nonlinear and even multivariate measure of causality, where multivariate measure is considered a measure that for the estimation of a driving-response relationship of two of the observed variables, the other observed variables are also considered. When the measure is computed for all directed pairs of observed variables, a complex network is formed, called also connectivity or causality network, where the nodes are the observed variables, and the connections are the estimated inter-dependences. For a network with binary connections the interdependences are discretized to zero (not significant) and one (significant) by applying a criterion for the significance, e.g., arbitrary threshold or statistical testing.