Identifying source of an information in complex networks with limited observation nodes

A rumor spreading in a social network or a disease propagating in a community can be described by a contact network whose nodes are persons or centers of contagion and links heterogeneous relations among them. Suppose that a rumor or a disease originating from a single source among a set of suspects spreads in a network, how to root out this rumor/disease source? This problem is important and challenging in different contexts of computer or social networks, which is made more difficult in many applications where we have access only to a limited set of observations. We study the problem of estimating the origin of a rumor/epidemic outbreak: given a contact network and a snapshot of epidemic spread at a certain time, determine the infection source. Assuming that the epidemic spread follows the usual susceptible-infected (SI) model, we introduce an inference algorithm based on sparsely placed observers. We present a strategy and show that it leads to significant improvement of performance compared to existing approaches. Our analysis sheds insight into the behavior of the rumor/epidemic spreading process not only in the local particular regime but also for the whole general regime.