Bootstrap percolation on spatial networks

TL;DR

This study investigates bootstrap percolation on Kleinberg's spatial networks, revealing a critical exponent at -1 that determines the nature of phase transitions and enhancing understanding of information spread in spatial social systems.

Contribution

The paper introduces a numerical analysis of bootstrap percolation on Kleinberg's spatial networks, identifying a critical exponent and characterizing phase transition behaviors.

Findings

Critical exponent at α_c = -1 for phase transition types.

Hybrid phase transition observed above α_c.

Behavioral spreading dynamics depend on the exponent α.

Abstract

We numerically study bootstrap percolation on Kleinberg's spatial networks, in which the probability density function of a node to have a long-range link at distance $r$ scales as $P(r)\sim r^{\alpha}$. Setting the ratio of the size of the giant active component to the network size as the order parameter, we find a critical exponent $\alpha_{c}=-1$, above which a hybrid phase transition is observed, with both the first-order and second-order critical points being constant. When $\alpha<\alpha_{c}$, the second-order critical point increases as the decreasing of $\alpha$, and there is either absent of the first-order phase transition or with a decreasing first-order critical point as the decreasing of $\alpha$, depending on other parameters. Our results expand the current understanding on the spreading of information and the adoption of behaviors on spatial social networks.