  Algorithms for identifying the infection states of nodes in a network are
crucial for understanding and containing infections. Often, however, only a
relatively small set of nodes have a known infection state. Moreover, the
length of time that each node has been infected is also unknown. This missing
data -- infection state of most nodes and infection time of the unobserved
infected nodes -- poses a challenge to the study of real-world cascades.
  In this work, we develop techniques to identify the latent infected nodes in
the presence of missing infection time-and-state data. Based on the likely
epidemic paths predicted by the simple susceptible-infected epidemic model, we
propose a measure (Infection Betweenness) for uncovering these unknown
infection states. Our experimental results using machine learning algorithms
show that Infection Betweenness is the most effective feature for identifying
latent infected nodes.
