Matching dependencies were recently introduced as declarative rules for data
cleaning and entity resolution. Enforcing a matching dependency on a database
instance identifies the values of some attributes for two tuples, provided that
the values of some other attributes are sufficiently similar. Assuming the
existence of matching functions for making two attributes values equal, we
formally introduce the process of cleaning an instance using matching
dependencies, as a chase-like procedure.
Ever since Kempe, Klienberg and Tardos (KKT) published their seminal paper on
maximizing the spread of influence in a social network, there has been
substantial work in this area. In the context of propagations in a social
graph, we can identify three orthogonal dimensions -- the number of seed nodes
activated at the beginning (known as budget), the (expected) number of
activated nodes at the end of the propagation (known as spread or coverage),
and the time taken for the propagation. We can constrain one or two of these
and try to optimize the third.