  The analysis of observed time series from nonlinear systems is usually done
by making a time-delay reconstruction to unfold the dynamics on a
multi-dimensional state space. An important aspect of the analysis is the
choice of the correct embedding dimension. The conventional procedure used for
this is either the method of false nearest neighbors or the saturation of some
invariant measure, such as, correlation dimension. Here we examine this issue
from a complex network perspective and propose a recurrence network based
measure to determine the acceptable minimum embedding dimension to be used for
such analysis. The measure proposed here is based on the well known
Kullback-Leibler divergence commonly used in information theory. We show that
the measure is simple and direct to compute and give accurate result for short
time series. To show the significance of the measure in the analysis of
practical data, we present the analysis of two EEG signals as examples.
