Information dissemination and confusion in signed networks

TL;DR

This paper models how information spreads in signed networks and aims to minimize confusion among actors, providing bounds on the number of confused actors and highlighting inherent limitations in information dissemination strategies.

Contribution

It introduces a new model for information dissemination in signed networks and establishes upper bounds on actor confusion, revealing fundamental limits of information accuracy.

Findings

Almost 60% of actors can be confused regardless of strategy.

Confusion bounds are established for signed networks and their equivalence classes.

Certain signed networks inherently lead to high confusion levels.

Abstract

We introduce a model of information dissemination in signed networks. It is a discrete-time process in which uninformed actors incrementally receive information from their informed neighbors or from the outside. Our goal is to minimize the number of confused actors - that is, the number of actors who receive contradictory information. We prove upper bounds for the number of confused actors in signed networks and in equivalence classes of signed networks. In particular, we show that there are signed networks where, for any information placement strategy, almost 60\% of the actors are confused. Furthermore, this is also the case when considering the minimum number of confused actors within an equivalence class of signed graphs.