The long-term impact of ranking algorithms in growing networks
Shilun Zhang, Mat\'u\v{s} Medo, Linyuan L\"u, Manuel Sebastian Mariani

TL;DR
This paper introduces a new network growth model to analyze the systemic effects of ranking algorithms, revealing that correcting for age bias enhances quality-popularity alignment and diversity in long-term network structures.
Contribution
The study presents a novel model to compare network properties under different ranking algorithms and proposes a ranking perturbation method to promote popularity diversity.
Findings
Correcting age bias improves quality-popularity correlation.
Ranking perturbation increases diversity in long-term popularity.
Networks become less concentrated with bias correction.
Abstract
When we search online for content, we are constantly exposed to rankings. For example, web search results are presented as a ranking, 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 the 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…
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