Empirical Bayes approaches to PageRank type algorithms for rating scientific journals

TL;DR

This paper introduces an empirical Bayes approach to PageRank algorithms for ranking scientific journals, providing a new method to distinguish zero types and adapt damping factors, demonstrated on citation data.

Contribution

It proposes a Bayesian reinterpretation of PageRank smoothing, allowing journal-specific damping factors and improved handling of zero citation cases.

Findings

New Bayesian PageRank model with journal-specific damping factors

Effective distinction between structural and sampling zeros

Application to 47 statistical journals demonstrating the method

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 PageRank type algorithms on the cross-citations transition matrix of the citing-cited network. The PageRank 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 $\alpha= 0.85$). 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 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 each journal. The procedures are illustrated with an example about cross-citations among 47 statistical journals studied by Varin et. al. (2016).