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- Bohlin, Ludvig, et al.
(författare)
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Robustness of journal rankings by network flows with different amounts of memory
- 2016
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Ingår i: Journal of the Association for Information Science and Technology. - : Wiley. - 2330-1635 .- 2330-1643. ; 67:10, s. 2527-2535
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Tidskriftsartikel (refereegranskat)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. |
- Rosvall, Martin, et al.
(författare)
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Memory in network flows and its effects on community detection, ranking, and spreading
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Random walks on networks is the standard tool for modelling spreading processes in social and biological systems. This first-order Markovapproach is used in conventional community detection, ranking, and spreading analysis although it ignores a potentially important feature ofthe dynamics: where flow moves to may depend on where it comes from. Here we analyse pathways from different systems, and while weonly observe marginal consequences for disease spreading, we show that ignoring the effects of second-order Markov dynamics has importantconsequences for community detection, ranking, and information spreading. For example, capturing dynamics with a second-order Markovmodel allows us to reveal actual travel patterns in air traffic and to uncover multidisciplinary journals in scientific communication. Thesefindings were achieved only by using more available data and making no additional assumptions, and therefore suggest that accounting forhigher-order memory in network flows can help us better understand how real systems are organized and function.
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| 3. |
- Viamontes Esquivel, Alcides, 1982-
(författare)
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Narrowing the gap between network models and real complex systems
- 2014
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Simple network models that focus only on graph topology or, at best, basic interactions are often insufficient to capture all the aspects of a dynamic complex system. In this thesis, I explore those limitations, and some concrete methods of resolving them. I argue that, in order to succeed at interpreting and influencing complex systems, we need to take into account slightly more complex parts, interactions and information flows in our models.This thesis supports that affirmation with five actual examples of applied research. Each study case takes a closer look at the dynamic of the studied problem and complements the network model with techniques from information theory, machine learning, discrete maths and/or ergodic theory. By using these techniques to study the concrete dynamics of each system, we could obtain interesting new information. Concretely, we could get better models of network walks that are used on everyday applications like journal ranking. We could also uncover asymptotic characteristics of an agent-based information propagation model which we think is the basis for things like belief propaga-tion or technology adoption on society. And finally, we could spot associations between antibiotic resistance genes in bacterial populations, a problem which is becoming more serious every day.
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