Probabilistic Modeling of Information Diffusion in Online Social Networks (OSN): An Empirical Study

Andrea da Silva, Patrick Mukala

Abstract


Epidemics or diffusion models are popular as a potentially effective solution to capture information diffusion in online social networks (OSNs). Moreover, these models have been widely studied in the context of mobile ad-hoc networks, wireless sensor networks and peer-to-peer technologies for security and information diffusion purposes. In this paper, we describe a formal probabilistic model for information diffusion in online social networks. We consider specifically how new behaviours spread from user to user through an OSN. We use PRISM for the formal analysis of the diffusion process on a fixed OSN topology. We show some experiemental results pertaining to the speed and the probability of infection cascade as global properties considered in the formal analysis of the diffusion model.

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