2018/02/14 by Caitlin Gray, Lewis Mitchell, Gray, Caitlin +3
Computer Science · Physics and Astronomy · #Complex Network Analysis Techniques #Data Analysis #FOS: Computer and information sciences #FOS: Physical sciences #Opinion Dynamics and Social Influence #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI) #Spam and Phishing Detection #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.1802.05039
openalex publication_date 2018/02/14 · openalex created_date 2022/10/02 · openalex updated_date 2026/07/28
Modelling information cascades over online social networks is important in\nfields from marketing to civil unrest prediction, however the underlying\nnetwork structure strongly affects the probability and nature of such cascades.\nEven with simple cascade dynamics the probability of large cascades are almost\nentirely dictated by network properties, with well-known networks such as\nErdos-Renyi and Barabasi-Albert producing wildly different cascades from the\nsame model. Indeed, the notion of 'superspreaders' has arisen to describe\nhighly influential nodes promoting global cascades in a social network. Here we\nuse a simple model of global cascades to show that the presence of locality in\nthe network increases the probability of a global cascade due to the increased\nvulnerability of connecting nodes. Rather than 'super-spreaders', we find that\nthe presence of these highly connected 'super-blockers' in heavy-tailed\nnetworks in fact reduces the probability of global cascades, while promoting\ninformation spread when targeted as the initial spreader.\n