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Contribution Details

Type Journal Article
Scope Discipline-based scholarship
Title Algorithmic bias amplification via temporal effects: The case of PageRank in evolving networks
Organization Unit
  • Mengtian Cui
  • Manuel Mariani
  • Matúš Medo
Item Subtype Original Work
Refereed Yes
Status Published in final form
  • English
Journal Title Communications in Nonlinear Science and Numerical Simulation
Publisher Elsevier
Geographical Reach international
ISSN 1007-5704
Volume 104
Page Range 106029
Date 2022
Abstract Text Biases impair the effectiveness of algorithms. For example, the age bias of the widely-used PageRank algorithm impairs its ability to effectively rank nodes in growing networks. PageRank’s temporal bias cannot be fully explained by existing analytic results that predict a linear relation between the expected PageRank score and the indegree of a given node. We show that in evolving networks, under a mean-field approximation, the expected PageRank score of a node can be expressed as the product of the node’s indegree and a previously-neglected age factor which can “amplify” the indegree’s age bias. We use two well-known empirical networks to show that our analytic results explain the observed PageRank’s age bias and, when there is an age bias amplification, they enable estimates of the node PageRank score that are more accurate than estimates based solely on local structural information. Accuracy gains are larger in degree-degree correlated networks, as revealed by a growing directed network model with tunable assortativity. Our approach can be used to analytically study other kinds of ranking bias.
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Digital Object Identifier 10.1016/j.cnsns.2021.106029
Other Identification Number merlin-id:21486
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