  The degree distribution is an important characteristic of complex networks.
In many applications, quantification of degree distribution in the form of a
fixed-length feature vector is a necessary step. On the other hand, we often
need to compare the degree distribution of two given networks and extract the
amount of similarity between the two distributions. In this paper, we propose a
novel method for quantification of the degree distributions in complex
networks. Based on this quantification method,a new distance function is also
proposed for degree distributions, which captures the differences in the
overall structure of the two given distributions. The proposed method is able
to effectively compare networks even with different scales, and outperforms the
state of the art methods considerably, with respect to the accuracy of the
distance function.
