Super-blockers and the effect of network structure on information cascades

TL;DR

This paper investigates how different network structures influence information cascade dynamics, revealing that highly connected 'super-blockers' can reduce the likelihood of large cascades but enhance targeted spreading.

Contribution

It introduces the concept of 'super-blockers' and demonstrates their impact on cascade probability and spread in various network topologies.

Findings

Locality increases global cascade probability.

Super-blockers reduce overall cascade likelihood.

Targeting super-blockers enhances initial spread.

Abstract

Modelling information cascades over online social networks is important in fields from marketing to civil unrest prediction, however the underlying network structure strongly affects the probability and nature of such cascades. Even with simple cascade dynamics the probability of large cascades are almost entirely dictated by network properties, with well-known networks such as Erdos-Renyi and Barabasi-Albert producing wildly different cascades from the same model. Indeed, the notion of 'superspreaders' has arisen to describe highly influential nodes promoting global cascades in a social network. Here we use a simple model of global cascades to show that the presence of locality in the network increases the probability of a global cascade due to the increased vulnerability of connecting nodes. Rather than 'super-spreaders', we find that the presence of these highly connected 'super-blockers' in heavy-tailed networks in fact reduces the probability of global cascades, while promoting information spread when targeted as the initial spreader.