Abstract
Studying diffusion process in complex networks has become an important issue nowadays. This issue has been addressed for different objectives, including quickly detecting the diffusion outbreak, blocking the propagation, and localizing the diffusion source. In this paper, we are mainly interested in developing an efficient algorithm to estimate both the source and the start time of the diffusion, under the constraint that only a subset of nodes can be observed. In doing so, we use the Ordinary Least Squares method (OLS) on the data gathered at observers, taking advantage of the linear correlation between the relative infection time of a node and its effective distance from the source (Brockman [2]). The proposed algorithm ensures an estimation at few hops from the actual source. We show its efficiency through numerical simulations on both synthetic and real networks.
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