  Diversity is a concept relevant to numerous domains of research varying from
ecology, to information theory, and to economics, to cite a few. It is a notion
that is steadily gaining attention in the information retrieval, network
analysis, and artificial neural networks communities. While the use of
diversity measures in network-structured data counts a growing number of
applications, no clear and comprehensive description is available for the
different ways in which diversities can be measured. In this article, we
develop a formal framework for the application of a large family of diversity
measures to heterogeneous information networks (HINs), a flexible, widely-used
network data formalism. This extends the application of diversity measures,
from systems of classifications and apportionments, to more complex relations
that can be better modeled by networks. In doing so, we not only provide an
effective organization of multiple practices from different domains, but also
unearth new observables in systems modeled by heterogeneous information
networks. We illustrate the pertinence of our approach by developing different
applications related to various domains concerned by both diversity and
networks. In particular, we illustrate the usefulness of these new proposed
observables in the domains of recommender systems and social media studies,
among other fields.
