Measuring Diversity in Heterogeneous Information Networks
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Diversity is a concept relevant to numerous domains of research as diverse as ecology, information theory, and economics, to cite a few. It is a notion that is continuously gaining attention in the information retrieval, network analysis, and artificial neural networks communities. While the use of diversity measures in network-structured data finds a growing number of applications, no clear and comprehensive description is available for the different ways in which diversities can be measured in data described by these structures. 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 allows for an extension of 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 do not only provide an effective organization of multiple practices from different domains, but we also unearth new observables in systems modeled by heterogeneous information networks. The pertinence of the approach is illustrated by the development of 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.
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