Feature Extraction from Degree Distribution for Comparison and Analysis of Complex Networks

The degree distribution is an important characteristic of complex networks. In many data analysis applications, the networks should be represented as fixed-length feature vectors and therefore the feature extraction from the degree distribution is a necessary step. Moreover, many applications need a similarity function for comparison of complex networks based on their degree distributions. Such a similarity measure has many applications, including classification and clustering of network instances, evaluation of network sampling methods, anomaly detection and study of epidemic dynamics. The existing methods are unable to effectively capture the similarity of degree distributions, particularly when the corresponding networks have different sizes. In this paper, we propose a feature extraction method and a similarity function for the degree distributions in complex networks. We propose to calculate the feature values based on the mean and standard deviation of the node degrees in order to decrease the effect of the network size on the extracted features. Experiments on a wide range of real and artificial networks confirms the accuracy, stability and effectiveness of the proposed method.