Measuring Diversity in Heterogeneous Information Networks

Autor: Pedro Ramaciotti Morales, Fabien Tarissan, Rémy Poulain, Raphaël Fournier-S'niehotta, Robin Lamarche-Perrin, Lionel Tabourier
Přispěvatelé: Médialab (Sciences Po) (Médialab), Sciences Po (Sciences Po), ComplexNetworks, LIP6, Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut des Systèmes Complexes - Paris Ile-de-France (ISC-PIF), Centre National de la Recherche Scientifique (CNRS)-Institut Curie [Paris]-Sorbonne Université (SU)-École polytechnique (X)-École normale supérieure - Cachan (ENS Cachan)-Université Paris 1 Panthéon-Sorbonne (UP1), Centre d'études et de recherche en informatique et communications (CEDRIC), Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), Institut des Sciences sociales du Politique (ISP), Université Paris Nanterre (UPN)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Ecole Normale Supérieure Paris-Saclay (ENS Paris Saclay), Centre National de la Recherche Scientifique (CNRS)
Jazyk: angličtina
Rok vydání: 2021
Předmět:
FOS: Computer and information sciences
General Computer Science
Computer science
Computer Science - Artificial Intelligence
Ecology (disciplines)
media_common.quotation_subject
Computer Science - Information Theory
0102 computer and information sciences
02 engineering and technology
Recommender system
Information theory
01 natural sciences
[INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI]
Theoretical Computer Science
Computer Science - Information Retrieval
Computer Science - Computers and Society
Computers and Society (cs.CY)
0202 electrical engineering
electronic engineering
information engineering
Social media
media_common
Artificial neural network
Information Theory (cs.IT)
Data science
Artificial Intelligence (cs.AI)
010201 computation theory & mathematics
020201 artificial intelligence & image processing
Information Retrieval (cs.IR)
Network analysis
Diversity (politics)
Zdroj: Theoretical Computer Science
Theoretical Computer Science, Elsevier, 2021, 859, pp.80-115. ⟨10.1016/j.tcs.2021.01.013⟩
ISSN: 1879-2294
0304-3975
DOI: 10.1016/j.tcs.2021.01.013⟩
Popis: 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.
43 pages
Databáze: OpenAIRE
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