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.

中文翻译：

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测量异构信息网络中的多样性

多样性是一个与众多研究领域相关的概念，从生态学到信息论，再到经济学，仅举几例。这是一个在信息检索、网络分析和人工神经网络社区中不断受到关注的概念。虽然在网络结构化数据中使用多样性度量计算了越来越多的应用程序，但对于衡量多样性的不同方式，尚无明确而全面的描述。在本文中，我们开发了一个正式框架，用于将大量多样性措施应用于异构信息网络 (HIN)，这是一种灵活、广泛使用的网络数据形式。这扩展了多样性措施的应用，从分类和分配系统，可以更好地由网络建模的更复杂的关系。在这样做时，我们不仅提供了来自不同领域的多种实践的有效组织，而且还在由异构信息网络建模的系统中挖掘了新的可观察性。我们通过开发与多样性和网络相关的各种领域相关的不同应用程序来说明我们方法的相关性。特别是，我们说明了这些新提出的可观察量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相关的各种领域相关的不同应用程序来说明我们方法的相关性。特别是，我们说明了这些新提出的可观察量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相关的各种领域相关的不同应用程序来说明我们方法的相关性。特别是，我们说明了这些新提出的可观察量在推荐系统和社交媒体研究等领域的有用性。