This paper is about a novel method for transforming a time series into a complex network graph. The paper is very well organized and begins with two sections, an introduction and related references. An overview of previous work is provided, and the authors describe how the proposed method differs from previous ones.

In section 3, the method for constructing the network graph--which is based on the reconstructed phase method of constructing a network--is described, but the key point of the method is the definition of distance.

In section 4, some results of the experimental data are presented, including how the method was applied to constant, periodic, linear divergent, logistic, Henon, and random series.

Section 5--the most important section, in my opinion--summarizes the discussion. Here, all of the series mentioned in section 4 are reviewed, the networks are shown, and some changes in the distance are presented in order to show how the networks are affected. To me, this shows that the approach is flexible.

Finally, sections 6 and 7 present some conclusions, including the use of this method in other systems such as chaos and random systems.