  Research on influence maximization has often to cope with marketing needs
relating to the propagation of information towards specific users. However,
little attention has been paid to the fact that the success of an information
diffusion campaign might depend not only on the number of the initial
influencers to be detected but also on their diversity w.r.t. the target of the
campaign. Our main hypothesis is that if we learn seeds that are not only
capable of influencing but also are linked to more diverse (groups of) users,
then the influence triggers will be diversified as well, and hence the target
users will get higher chance of being engaged. Upon this intuition, we define a
novel problem, named Diversity-sensitive Targeted Influence Maximization
(DTIM), which assumes to model user diversity by exploiting only topological
information within a social graph. To the best of our knowledge, we are the
first to bring the concept of topology-driven diversity into targeted IM
problems, for which we define two alternative definitions. Accordingly, we
propose approximate solutions of DTIM, which detect a size-k set of users that
maximizes the diversity-sensitive capital objective function, for a given
selection of target users. We evaluate our DTIM methods on a special case of
user engagement in online social networks, which concerns users who are not
actively involved in the community life. Experimental evaluation on real
networks has demonstrated the meaningfulness of our approach, also highlighting
the opportunity of further development of solutions for DTIM applications.
