Empirical Bayes approaches to PageRank type algorithms for rating scientific journals

Jean-Louis Foulley 1 Gilles Celeux 2 Julie Josse 3
2 SELECT - Model selection in statistical learning
Inria Saclay - Ile de France, LMO - Laboratoire de Mathématiques d'Orsay, CNRS - Centre National de la Recherche Scientifique : UMR
Abstract : Following criticisms against the journal Impact Factor, new journal influence scores have been developed such as the Eigenfactor or the Prestige Scimago Journal Rank. They are based on PageR-ank type algorithms on the cross-citations transition matrix of the citing-cited network. The PageR-ank algorithm performs a smoothing of the transition matrix combining a random walk on the data network and a teleportation to all possible nodes with fixed probabilities (the damping factor being α = 0.85). We reinterpret this smoothing matrix as the mean of a posterior distribution of a Dirichlet-multinomial model in an empirical Bayes perspective. We suggest a simple yet efficient way to make a clear distinction between structural and sampling zeroes. This allows us to contrast cases with self-citations are included or excluded to avoid overvalued journal bias. We estimate the model parameters by maximizing the marginal likelihood with a Majorize-Minimize algorithm. The procedure ends up with a score similar to the PageRank ones but with a damping factor depending on the journal at hand. The procedures are illustrated with an example about cross-citations among 47 statistical journals studied by Varin et al. (2016).
Type de document :
Pré-publication, Document de travail
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Contributeur : Gilles Celeux <>
Soumis le : jeudi 8 juin 2017 - 17:48:05
Dernière modification le : jeudi 11 janvier 2018 - 06:27:31
Document(s) archivé(s) le : samedi 9 septembre 2017 - 13:34:19


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  • HAL Id : hal-01535134, version 1


Jean-Louis Foulley, Gilles Celeux, Julie Josse. Empirical Bayes approaches to PageRank type algorithms for rating scientific journals. 2017. 〈hal-01535134〉



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