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.

多样性是与生态学，信息论和经济学等众多研究领域相关的概念。在信息检索，网络分析和人工神经网络社区中，这一概念正逐渐得到关注。尽管在网络结构化数据中使用多样性度量的应用越来越多，但对于度量多样性的不同方法却没有清晰，全面的描述。在本文中，我们开发了一个正式的框架，用于将大量的多样性度量标准应用于异构信息网络（HIN），这是一种灵活的，广泛使用的网络数据形式。这从分类和分配系统扩展了多样性度量的应用，到可以通过网络更好地建模的更复杂的关系。这样，我们不仅可以有效地组织来自不同领域的多种实践，而且还可以在由异构信息网络建模的系统中发现新的可观察到的事物。我们通过开发与多样性和网络所涉及的各个领域相关的不同应用程序来说明我们方法的针对性。特别是，我们举例说明了这些新提出的可观测指标在推荐系统和社交媒体研究等领域的有用性。我们通过开发与多样性和网络所涉及的各个领域相关的不同应用程序来说明我们方法的针对性。特别是，我们举例说明了这些新提出的可观测指标在推荐系统和社交媒体研究等领域的有用性。我们通过开发与多样性和网络所涉及的各个领域相关的不同应用程序来说明我们方法的针对性。特别是，我们举例说明了这些新提出的可观测指标在推荐系统和社交媒体研究等领域的有用性。