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

TL;DR

This paper introduces a new feature extraction method and similarity function for degree distributions in complex networks, improving comparison accuracy across networks of different sizes.

Contribution

The paper proposes a size-invariant feature extraction and similarity measure based on mean and standard deviation of node degrees, enhancing network comparison methods.

Findings

Outperforms existing methods in accuracy of network similarity measurement.

Effective in comparing networks of varying sizes.

Validated on artificial and real-world network datasets.

Abstract

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. Based on our observations about the structure of the degree distributions in networks over time, we propose a feature extraction 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. The proposed method is evaluated using different artificial and real network datasets, and it outperforms the state of the art methods with respect to the accuracy of the distance function and the effectiveness of the extracted features.