Decelerated spreading in degree-correlated networks

TL;DR

This paper investigates how degree correlations in networks influence the speed of spreading phenomena, revealing that positive correlations slow down propagation while negative correlations can accelerate it, with implications for controlling real-world processes.

Contribution

It introduces a tunable spreading model on scale-free networks and explains the impact of degree correlations on propagation speed using efficient paths and k-core decomposition.

Findings

Positive degree correlations slow spreading in networks.

Negative degree correlations can accelerate propagation.

Efficient paths and k-core analysis explain the effects.

Abstract

While degree correlations are known to play a crucial role for spreading phenomena in networks, their impact on the propagation speed has hardly been understood. Here we investigate a tunable spreading model on scale-free networks and show that the propagation becomes slow in positively (negatively) correlated networks if nodes with a high connectivity locally accelerate (decelerate) the propagation. Examining the efficient paths offers a coherent explanation for this result, while the $k$-core decomposition reveals the dependence of the nodal spreading efficiency on the correlation. Our findings should open new pathways to delicately control real-world spreading processes.