  Ordinal measures provide a valuable collection of tools for analyzing
correlated data series. However, using these methods to understand the
information interchange in networks of dynamical systems, and uncover the
interplay between dynamics and structure during the synchronization process,
remains relatively unexplored. Here, we compare the ordinal permutation
entropy, a standard complexity measure in the literature, and the permutation
entropy of the ordinal transition probability matrix that describes the
transitions between the ordinal patterns derived from a time series. We find
that the permutation entropy based on the ordinal transition matrix outperforms
the rest of the tested measures in discriminating the topological role of
networked chaotic R\"ossler systems. Since the method is based on permutation
entropy measures, it can be applied to arbitrary real-world time series
exhibiting correlations originating from an existing underlying unknown network
structure. In particular, we show the effectiveness of our method using
experimental datasets of networks of nonlinear oscillators.
