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Modeling the spread of infections on networks is a well-studied and importantfield of research. Most infection and diffusion models require a real value orprobability on the edges of the network as an input, but this is rarelyavailable in real-life applications. Our goal in this paper is to develop ageneral framework for this task. The general model works with the most widelyused infection models and is able to handle an arbitrary number of observationson such processes. The model is defined as a general optimization task and aParticle Swarm heuristic is proposed to solve it. We evaluate the accuracy andspeed of the proposed method on a high variety of realistic infectionscenarios.

A generalized framework for the estimation of edge infection probabilities