Non-backtracking PageRank: From the classic model to hashimoto matrices

David Aleja, Regino Criado, Alejandro J. García del Amo, Ángel Pérez and Miguel Romance

Chaos, Solitons & Fractals, 2019, vol. 126, issue C, 283-291

Abstract: Non-backtracking centrality was introduced as a way to correct what may be understood as a deficiency in the eigenvector centrality, since the eigenvector centrality in a network can be artificially increased in high-degree nodes (hubs) because a hub is central because its neighbors are central, but these, in turn, are central just because they are hub neighbors. We define the non-backtracking PageRank as a new measure modifying the well-known classic PageRank in order to avoid the possibility of the random walker returning to the node immediately visited (non-backtracking walk). But, as we show, this measure presents a gap and a remarkable difference between the limit of “no penalty for return trips” and the direct calculation of the non-backtracking PageRank. Also, as it is shown in the applications presented, in certain cases this new measure produces notable variations with respect to the classifications obtained by the classic PageRank.

Keywords: Non-backtracking PageRank; Non-backtracking centrality; PageRank centrality (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:126:y:2019:i:c:p:283-291

DOI: 10.1016/j.chaos.2019.06.017

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