Super-blockers and the Effect of Network Structure on Information Cascades

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.

Bayesian Inference of Network Structure From Information Cascades

“…This is largely due to the nature of the cascades observed. It is well known that the underlying structure impacts the nature of the cascades simulated using various cascade models [18,23]. Even for constant β the underlying structure of the network has a dramatic impact on the size distribution of cascades we observe.…”

Logic and learning in network cascades

“…A global cascade is said to occur if the cascade size attains a predetermined fixed fraction of the network (σ (A) ≥ ), where ∈ [0, 1] is a constant. For example, in prior work, a value of = 0.1 has been used (Gray et al, 2018;Watts, 2002), so we would be evaluating σ (A) ≥ 0.1 for the global cascade (Gray et al, 2018;Kempe et al, 2003;Watts, 2002).…”

“…Our interest is again in the simplest possible biologically motivated model of cascades and what it can achieve. Finally, there has been work on percolation in spatial networks, but also not as learning (Barthélemy, 2011;Gao et al, 2015;Gray et al, 2018;Penrose, 2003).…”

Complex Contagion Features without Social Reinforcement in a Model of Social Information Flow

“…Often denser networks lead to less spread, unlike simple contagion where a contagion will spread more easily as denser networks afford more opportunities (links) for spreading. Another feature of complex contagion is the complicated role of clustering where clustering can appear to either promote or inhibit contagion [ 25 , 26 , 27 , 28 ]. Complex contagion also exhibits a “weakness of long ties” effect, where long ties impede the flow of contagion [ 29 ], in contrast with the seminal “strength of weak ties” result [ 30 ] that implies long-range ties have an out-sized role in promoting information flow.…”