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As the number of scientific journals has multiplied, journal rankings havebecome increasingly important for scientific decisions. From submissions andsubscriptions to grants and hirings, researchers, policy makers, and fundingagencies make important decisions with influence from journal rankings such asthe ISI journal impact factor. Typically, the rankings are derived from thecitation network between a selection of journals and unavoidably depend on thisselection. However, little is known about how robust rankings are to theselection of included journals. Here we compare the robustness of three journalrankings based on network flows induced on citation networks. They modelpathways of researchers navigating scholarly literature, stepping betweenjournals and remembering their previous steps to different degree: zero-stepmemory as impact factor, one-step memory as Eigenfactor, and two-step memory,corresponding to zero-, first-, and second-order Markov models of citation flowbetween journals. We conclude that higher-order Markov models perform betterand are more robust to the selection of journals. Whereas our analysisindicates that higher-order models perform better, the performance gain for thesecond-order Markov model comes at the cost of requiring more citation dataover a longer time period.

Robustness of journal rankings by network flows with different amounts of memory