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Empirical Bayes approaches to PageRank type algorithms for rating scientific journals

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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).
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Dates and versions

hal-01535134 , version 1 (08-06-2017)

Identifiers

  • HAL Id : hal-01535134 , version 1

Cite

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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