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Type | Journal Article |
Scope | Discipline-based scholarship |
Title | Network-based ranking in social systems: three challenges |
Organization Unit | |
Authors |
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Contributors |
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Item Subtype | Original Work |
Refereed | Yes |
Status | Published in final form |
Language |
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Journal Title | Journal of Physics: Complexity |
Publisher | IOP Publishing |
Geographical Reach | international |
ISSN | 2632-072X |
Volume | 1 |
Number | 1 |
Page Range | 011001 |
Date | 2020 |
Abstract Text | Ranking algorithms are pervasive in our increasingly digitized societies, with important real-world applications including recommender systems, search engines, and influencer marketing practices. From a network science perspective, network-based ranking algorithms solve fundamental problems related to the identification of vital nodes for the stability and dynamics of a complex system. Despite the ubiquitous and successful applications of these algorithms, we argue that our understanding of their performance and their applications to real-world problems face three fundamental challenges: (1) rankings might be biased by various factors; (2) their effectiveness might be limited to specific problems; and (3) agents’ decisions driven by rankings might result inpotentially vicious feedback mechanisms and unhealthy systemic consequences. Methods rooted innetwork science and agent-based modeling can help us to understand and overcome these challenges. |
Free access at | DOI |
Official URL | https://iopscience.iop.org/article/10.1088/2632-072X/ab8a61/pdf |
Related URLs | |
Digital Object Identifier | 10.1088/2632-072X/ab8a61 |
Other Identification Number | merlin-id:19510 |
PDF File | Download from ZORA |
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