  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 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 could be used as an informative
tool for the design of improved information filtering tools.
