Uncovering Evolutionary Ages of Nodes in Complex Networks

TL;DR

This paper introduces a spectral analysis method to estimate the ages of nodes in complex networks, revealing their evolutionary history and providing insights into network development.

Contribution

The paper presents a novel spectral analysis approach combined with compressive sensing to determine node ages and infer network evolution.

Findings

Eigenvalue magnitudes correlate with node ages.

Spectral analysis can uncover network topology from short time series.

Method applies to both model and real-world networks.

Abstract

In a complex network, different groups of nodes may have existed for different amounts of time. To detect the evolutionary history of a network is of great importance. We present a general method based on spectral analysis to address this fundamental question in network science. In particular, we argue and demonstrate, using model and real-world networks, the existence of positive correlation between the magnitudes of eigenvalues and node ages. In situations where the network topology is unknown but short time series measured from nodes are available, we suggest to uncover the network topology at the present (or any given time of interest) by using compressive sensing and then perform the spectral analysis. Knowledge of ages of various groups of nodes can provide significant insights into the evolutionary process underpinning the network.