Jamming and Correlation Patterns in Traffic of Information on Sparse Modular Networks

TL;DR

This paper investigates how information traffic behaves on sparse modular networks, revealing how modules influence jamming transitions and correlation patterns, with implications for analyzing real-world complex networks.

Contribution

It introduces a detailed analysis of traffic flow and correlations in modular networks, highlighting the role of modules in jamming and the spectral signatures of modularity.

Findings

Modules significantly affect the jamming transition.

Correlation matrices reflect network modularity.

Enhanced correlations occur between modular hubs.

Abstract

We study high-density traffic of information packets on sparse modular networks with scale-free subgraphs. With different statistical measures we distinguish between the free flow and congested regime and point out the role of modules in the jamming transition. We further consider correlations between traffic signals collected at each node in the network. The correlation matrix between pairs of signals reflects the network modularity in the eigenvalue spectrum and the structure of eigenvectors. The internal structure of the modules has an important role in the diffusion dynamics, leading to enhanced correlations between the modular hubs, which can not be filtered out by standard methods. Implications for the analysis of real networks with unknown modular structure are discussed.