Metric projection for dynamic multiplex networks

TL;DR

This paper introduces a metric-based method to analyze the evolution of multiplex networks over time, enabling the detection of structural changes in complex dynamic systems.

Contribution

It proposes a novel two-step strategy using network distance metrics to transform multiplex network time series into analyzable real-valued sequences.

Findings

Effective in detecting structural changes in synthetic data

Successfully applied to Gulf dataset of political events

Demonstrates utility in real-world dynamic network analysis

Abstract

Evolving multiplex networks are a powerful model for representing the dynamics along time of different phenomena, such as social networks, power grids, biological pathways. However, exploring the structure of the multiplex network time series is still an open problem. Here we propose a two-steps strategy to tackle this problem based on the concept of distance (metric) between networks. Given a multiplex graph, first a network of networks is built for each time steps, and then a real valued time series is obtained by the sequence of (simple) networks by evaluating the distance from the first element of the series. The effectiveness of this approach in detecting the occurring changes along the original time series is shown on a synthetic example first, and then on the Gulf dataset of political events.