  Embedding diversity into knowledge discovery tasks is of crucial importance
to enhance the meaningfulness of the mined patterns with high-impact aspects
related to novelty, serendipity, and ethics. Surprisingly, in the classic
problem of influence maximization in social networks, relatively little study
has been devoted to diversity and its integration into the objective function
of an influence maximization method.
  In this work, we propose the integration of a side-information-based notion
of seed diversity into the objective function of a targeted influence
maximization problem. Starting from the assumption that side-information is
available at node level in the general form of categorical attribute values, we
design a class of monotone submodular functions specifically conceived for
determining the diversity within a set of categorical profiles associated with
the seeds to be discovered. This allows us to develop an efficient scalable
approximate method, with a constant-factor guarantee of optimality. More
precisely, we formulate the attribute-based diversity-sensitive targeted
influence maximization problem under the state-of-the-art reverse influence
sampling framework, and we develop a method, dubbed ADITUM, that ensures a
(1-1/e-\epsilon)-approximate solution under the general triggering diffusion
model. We experimentally evaluated ADITUM on five real-world networks,
including comparison with methods that exploit numerical-attribute-based
diversity and topology-driven diversity in influence maximization.
