Measuring Diversity in Heterogeneous Information Networks

By

Pedro Ramaciotti Morales , Robin Lamarche-Perrin, Raphaël Fournier-S'Niehotta, Rémy Poulain, Lionel Tabourier,  Fabien Tarissan

Theoretical Computer Science, 2021

Abstract

Diversity is a concept relevant to numerous domains of research varying from ecology, to information theory, andto economics, to cite a few. It is a notion that is steadily gaining attention in the information retrieval, networkanalysis, and artificial neural networks communities. While the use of diversity measures in network-structured datacounts a growing number of applications, no clear and comprehensive description is available for the different waysin which diversities can be measured. In this article, we develop a formal framework for the application of a largefamily of diversity measures to heterogeneous information networks (HINs), a flexible, widely-used network dataformalism. 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 effectiveorganization of multiple practices from different domains, but also unearth new observables in systems modeled byheterogeneous information networks. We illustrate the pertinence of our approach by developing different applicationsrelated to various domains concerned by both diversity and networks. In particular, we illustrate the usefulness of thesenew proposed observables in the domains of recommender systems and social media studies, among other fields.

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