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

and clustering of network instances, evaluation of network sampling methods, anomaly detection, and study of epidemic dynamics. The existing methods are unable to eectively capture the similarity of degree distributions, particularly when the corresponding networks have dierent 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 eect of the network size on the extracted features. Experiments on a wide range of real and articial networks conrms the accuracy, stability, and eectiveness of the proposed method.