Characterizing information importance and the effect on the spread in various graph topologies

TL;DR

This paper introduces a mathematical model for information spread across different media and graph topologies, accounting for conflicting info and topical popularity, validated through simulations and real-world data comparison.

Contribution

It presents a novel combined model of information diffusion and graph-based spread, incorporating conflicting information and topical relevance, validated with simulations and real-world data.

Findings

Model accurately predicts information spread patterns

Simulations reveal optimal graph structures for dissemination

Real-world data confirms model relevance

Abstract

In this paper we present a thorough analysis of the nature of news in different mediums across the ages, introducing a unique mathematical model to fit the characteristics of information spread. This model enhances the information diffusion model to account for conflicting information and the topical distribution of news in terms of popularity for a given era. We translate this information to a separate graphical node model to determine the spread of a news item given a certain category and relevance factor. The two models are used as a base for a simulation of information dissemination for varying graph topoligies. The simulation is stress-tested and compared against real-world data to prove its relevancy. We are then able to use these simulations to deduce some conclusive statements about the optimization of information spread.