| 1. |
- Bohlin, Ludvig, et al.
(author)
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Robustness of journal rankings by network flows with different amounts of memory
- 2016
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In: Journal of the Association for Information Science and Technology. - : Wiley. - 2330-1635 .- 2330-1643. ; 67:10, s. 2527-2535
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Journal article (peer-reviewed)abstract
- As the number of scientific journals has multiplied, journal rankings have become increasingly important for scientific decisions. From submissions and subscriptions to grants and hirings, researchers, policy makers, and funding agencies make important decisions influenced by journal rankings such as the ISI journal impact factor. Typically, the rankings are derived from the citation network between a selection of journals and unavoidably depend on this selection. However, little is known about how robust rankings are to the selection of included journals. We compare the robustness of three journal rankings based on network flows induced on citation networks. They model pathways of researchers navigating the scholarly literature, stepping between journals and remembering their previous steps to different degrees: zero-step memory as impact factor, one-step memory as Eigenfactor, and two-step memory, corresponding to zero-, first-, and second-order Markov models of citation flow between journals. We conclude that higher-order Markov models perform better and are more robust to the selection of journals. Whereas our analysis indicates that higher-order models perform better, the performance gain for higher-order Markov models comes at the cost of requiring more citation data over a longer time period.
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| 2. |
- Kheirkhahzadeh, Masoumeh, et al.
(author)
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Efficient community detection of network flows for varying Markov times and bipartite networks
- 2016
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In: Physical Review E. - 2470-0045. ; 93:3
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Journal article (peer-reviewed)abstract
- Community detection of network flows conventionally assumes one-step dynamics on the links. For sparse networks and interest in large-scale structures, longer timescales may be more appropriate. Oppositely, for large networks and interest in small-scale structures, shorter timescales may be better. However, current methods for analyzing networks at different timescales require expensive and often infeasible network reconstructions. To overcome this problem, we introduce a method that takes advantage of the inner workings of the map equation and evades the reconstruction step. This makes it possible to efficiently analyze large networks at different Markov times with no extra overhead cost. The method also evades the costly unipartite projection for identifying flow modules in bipartite networks.
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