  Recent work on modeling influence propagation focus on progressive models,
i.e., once a node is influenced (active) the node stays in that state and
cannot become inactive. However, this assumption is unrealistic in many
settings where nodes can transition between active and inactive states. For
instance, a user of a social network may stop using an app and become inactive,
but again activate when instigated by a friend, or when the app adds a new
feature or releases a new version. In this work, we study such non-progressive
phenomena and propose an efficient model of influence propagation.
Specifically, we model in influence propagation as a continuous-time Markov
process with 2 states: active and inactive. Such a model is both highly
scalable (we evaluated on graphs with over 2 million nodes), 17-20 times
faster, and more accurate for estimating the spread of influence, as compared
with state-of-the-art progressive models for several applications where nodes
may switch states.
