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Contribution Details

Type Journal Article
Scope Discipline-based scholarship
Title The long-term impact of ranking algorithms in growing networks
Organization Unit
  • Shilun Zhang
  • Matúš Medo
  • Linyuan Lü
  • Manuel Mariani
Item Subtype Original Work
Refereed Yes
Status Published in final form
  • English
Journal Title Information Sciences
Publisher Elsevier
Geographical Reach international
ISSN 0020-0255
Volume 488
Page Range 257 - 271
Date 2019
Abstract Text When users search online for content, they are constantly exposed to rankings. For example, web search results are presented as a ranking of relevant websites, and online bookstores often show us lists of best-selling books. While popularity-based ranking algorithms (like Google’s PageRank) have been extensively studied in previous works, we still lack a clear understanding of their potential systemic consequences. In this work, we fill this gap by introducing a new model of network growth that allows us to compare the properties of networks generated under the influence of different ranking algorithms. We show that by correcting for the omnipresent age bias of popularity-based ranking algorithms, the resulting networks exhibit a significantly larger agreement between the nodes’ inherent quality and their long-term popularity, and a less concentrated popularity distribution. To further promote popularity diversity, we introduce and validate a perturbation of the original rankings where a small number of randomly-selected nodes are promoted to the top of the ranking. Our findings move the first steps toward a model-based understanding of the long-term impact of popularity-based ranking algorithms, and our novel framework could be used to design improved information filtering tools.
Digital Object Identifier 10.1016/j.ins.2019.03.021
Other Identification Number merlin-id:17705
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