Extracting diffusion channels from real-world social data: A delay-agnostic learning of transmission probabilities

Probabilistic cascade models consider information diffusion as an iterative process in which information transits from users to others in a network. The problem of diffusion modeling then comes down to learning transmission probability distributions, depending on hidden influence relationships between users, in order to discover the main diffusion channels of the network. Various learning models have been proposed in the literature, but we argue that the diffusion mechanisms defined in most of these models are too complex for real social networks, where transmissions of content occur between human users. Classical models usually have some difficulties for extracting the main regularities in such real-world settings. In this paper, we propose a relaxed learning process of the well-known Independent Cascade model that, rather than attempting to explain exact timestamps of users' infections, focus on infection probabilities knowing sets of previously infected users. Experiments show the effectiveness of our proposals, by considering the learned models for real-world prediction tasks.