2014/05/30 by Ludvig Bohlin, Alcides Viamontes Esquivel, Bohlin, Ludvig +5
Computer Science · Decision Sciences · Physics and Astronomy · #Complex Network Analysis Techniques #Digital Libraries (cs.DL) #FOS: Computer and information sciences #FOS: Physical sciences #Game Theory and Applications #Peer-to-Peer Network Technologies #Physics and Society (physics.soc-ph) #scientometrics and bibliometrics research
paper · pdf · doi:10.48550/arxiv.1405.7832
openalex publication_date 2014/05/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
As the number of scientific journals has multiplied, journal rankings have\nbecome increasingly important for scientific decisions. From submissions and\nsubscriptions to grants and hirings, researchers, policy makers, and funding\nagencies make important decisions with influence from journal rankings such as\nthe ISI journal impact factor. Typically, the rankings are derived from the\ncitation network between a selection of journals and unavoidably depend on this\nselection. However, little is known about how robust rankings are to the\nselection of included journals. Here we compare the robustness of three journal\nrankings based on network flows induced on citation networks. They model\npathways of researchers navigating scholarly literature, stepping between\njournals and remembering their previous steps to different degree: zero-step\nmemory as impact factor, one-step memory as Eigenfactor, and two-step memory,\ncorresponding to zero-, first-, and second-order Markov models of citation flow\nbetween journals. We conclude that higher-order Markov models perform better\nand are more robust to the selection of journals. Whereas our analysis\nindicates that higher-order models perform better, the performance gain for the\nsecond-order Markov model comes at the cost of requiring more citation data\nover a longer time period.\n