  Information diffusion mechanisms based on social influence models are mainly
studied using likelihood of adoption when active neighbors expose a user to a
message. The problem arises primarily from the fact that for the most part,
this explicit information of who-exposed-whom among a group of active neighbors
in a social network, before a susceptible node is infected is not available. In
this paper, we attempt to understand the diffusion process through information
cascades by studying the temporal network structure of the cascades. In doing
so, we accommodate the effect of exposures from active neighbors of a node
through a network pruning technique that leverages network motifs to identify
potential infectors responsible for exposures from among those active
neighbors. We attempt to evaluate the effectiveness of the components used in
modeling cascade dynamics and especially whether the additional effect of the
exposure information is useful. Following this model, we develop an inference
algorithm namely InferCut, that uses parameters learned from the model and the
exposure information to predict the actual parent node of each potentially
susceptible user in a given cascade. Empirical evaluation on a real world
dataset from Weibo social network demonstrate the significance of incorporating
exposure information in recovering the exact parents of the exposed users at
the early stages of the diffusion process.
