A Novel Discrete-time Model of Information Diffusion on Social Networks Considering Users Behavior

TL;DR

This paper introduces the SDIR model, an extension of the SIR framework, to better capture user behavior in social networks, including delays and abstentions in information sharing.

Contribution

The paper proposes the SDIR model with a new delayable state and derives its dynamical equations, providing insights into information diffusion dynamics and control strategies.

Findings

SDIR model effectively captures user delay and abstention behaviors.

Derived stability conditions for the diffusion process.

Proposed an approximation algorithm for influence minimization.

Abstract

In this paper, we introduce the SDIR (Susceptible-Delayable-Infected-Recovered) model, an extension of the classical SIR epidemic framework, to provide a more explicit characterization of user behavior in online social networks. The newly merged state D (delayable) represents users who have received the information but delayed its spreading and may eventually choose not to share it at all. Based on the mean-field approximation method, we derive the dynamical equations of the model and investigate its convergence and stability conditions. Under these conditions, we further propose an approximation algorithm for the edge-deletion problem, aiming to minimize the influence of information diffusion by identifying approximate solutions.