The applications of infection models like the Linear Threshold or the Domingos-Richardson model requires a graph weighted with infection probabilities.In many real-life applications these probabilities are unknown; therefore a systematic method for the estimation of these probabilities is required.One of the methods proposed to solve this problem, the Inverse Infection Model, was originally formulated for estimating credit default in banking applications.In this paper we are going to test the capabilities of the Inverse Infection Model in a more controlled environment.We are going to use artificially created graphs to evaluate the speed and the accuracy of estimations.We are also going to examine how approximations and heuristics can be used to improve the speed of the calculations.Finally, we will experiment with the amount of a priori information available in the model and evaluate how well this method performs if only partial information is available.