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.
School of Computer Science（计算机科学学院）；Engineering, Sun Yat-sen University（工程，中山大学）；School of Software Engineering（软件工程学院）；Sun Yat-sen University（中山大学）；Zhuhai Key Laboratory of Trusted Large Language Models（珠海可信大语言模型实验室）；Tencent AI Lab（腾讯AI实验室）；National University of Singapore（新加坡国立大学）；Shenzhen University（深圳大学）；Shanghai Jiao Tong University（上海交通大学）
Southern University of Science and Technology（南方科技大学）；Tandon School of Engineering, New York University（纽约大学坦登工程学院）；School of Data Science, Lingnan University（岭南大学数据科学学院）
What and Whose Knowledge? Measuring Epistemic Diversity in Large Language Models
University of Copenhagen（哥本哈根大学）；Aalborg University Copenhagen（奥胡斯大学哥本哈根分校）；Stanford University（斯坦福大学）；University of Colorado Boulder（科罗拉多大学波德分校）；Microsoft Research（微软研究院）
HSFN: Hierarchical Selection for Fake News Detection building Heterogeneous Ensemble
Centro de Informática (CIn), Universidade Federal de Pernambuco (UFPE)（计算机学院（CIn），佛罗里达州立大学（UFPE））；École de Technologie Supérieure (ÉTS), Université du Québec（高级技术学院（ÉTS），魁北克大学）
