Probing models of information spreading in social networks

TL;DR

This paper uses signal processing and fractal analysis on a modified SIS model to understand information spreading in social networks, revealing how data can infer underlying memory effects.

Contribution

It introduces a modified SIS model incorporating synergy and influential nodes, linking fractal analysis to infer memory times from accessible data.

Findings

Fractal analysis can estimate the memory time of spreading mechanisms.

Modified SIS model captures key behaviors of information spreading.

Data analysis reveals underlying process parameters.

Abstract

We apply signal processing analysis to the information spreading in scale-free network. To reproduce typical behaviors obtained from the analysis of information spreading in the world wide web we use a modified SIS model where synergy effects and influential nodes are taken into account. This model depends on a single free parameter that characterize the memory-time of the spreading process. We show that by means of fractal analysis it is possible -from aggregated easily accessible data- to gain information on the memory time of the underlying mechanism driving the information spreading process.