Information spreading on dynamic social networks

TL;DR

This paper introduces a link rewiring strategy based on the Fermi function to model dynamic social networks, demonstrating that such dynamism accelerates and broadens information spread compared to static networks.

Contribution

It presents a novel link rewiring model that captures social network dynamism and analyzes its impact on information spreading using the SIR model.

Findings

Rewiring accelerates information spread and increases cascade size.

Early-stage spreading is crucial for overall dissemination.

Dynamic networks facilitate faster and broader information diffusion.

Abstract

Nowadays, information spreading on social networks has triggered an explosive attention in various disciplines. Most of previous works in this area mainly focus on discussing the effects of spreading probability or immunization strategy on static networks. However, in real systems, the peer-to-peer network structure changes constantly according to frequently social activities of users. In order to capture this dynamical property and study its impact on information spreading, in this paper, a link rewiring strategy based on the Fermi function is introduced. In the present model, the informed individuals tend to break old links and reconnect to their second-order friends with more uninformed neighbors. Simulation results on the susceptible-infected-recovered (\textit{SIR}) model with fixed recovery time $T=1$ indicate that the information would spread more faster and broader with the proposed rewiring strategy. Extensive analyses of the information cascade size distribution show that the spreading process of the initial steps plays a very important role, that is to say, the information will spread out if it is still survival at the beginning time. The proposed model may shed some light on the in-depth understanding of information spreading on dynamical social networks.