Abstract
We consider the problem of provenance identification for diffusion processes on social networks based on cascade information observed from a small set of nodes during the observation windows. The diffusion dynamics are described by the susceptible-infected (SI) model and a constrained maximum likelihood (ML) estimator is formulated to maximize the probability of the diffusion provenance. The identification approach consists of two steps: first, a pruning rule is defined to obtain a set of suspected provenance nodes from susceptible node observers; and then a correlation coefficient is maximized to find the provenance node from the candidate ones. Experiments on synthetic networks and real-world networks verify the effectiveness of our approach.
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