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Robustness of journal rankings by network flows with different amounts of memory
Umeå University, Faculty of Science and Technology, Department of Physics.
Umeå University, Faculty of Science and Technology, Department of Physics. (Icelab)
Umeå University, Faculty of Science and Technology, Department of Physics. (Icelab)
Umeå University, Faculty of Science and Technology, Department of Physics. (Icelab)
2016 (English)In: Journal of the Association for Information Science and Technology, ISSN 2330-1635, E-ISSN 2330-1643, Vol. 67, no 10, p. 2527-2535Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2016. Vol. 67, no 10, p. 2527-2535
National Category
Other Physics Topics Information Studies
Research subject
Physics
Identifiers
URN: urn:nbn:se:umu:diva-89147DOI: 10.1002/asi.23582ISI: 000384509100016OAI: oai:DiVA.org:umu-89147DiVA, id: diva2:719091
Note

Originally published in thesis in manuscript form.

Available from: 2014-05-22 Created: 2014-05-22 Last updated: 2018-05-14Bibliographically approved
In thesis
1. Narrowing the gap between network models and real complex systems
Open this publication in new window or tab >>Narrowing the gap between network models and real complex systems
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

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.

Place, publisher, year, edition, pages
Umeå: Umeå University, 2014. p. 62
Keyword
complex systems, network science, community detection, model selection, signficance analysis, ergodicity
National Category
Physical Sciences
Research subject
Physics
Identifiers
urn:nbn:se:umu:diva-89149 (URN)978-91-7601-085-3 (ISBN)
Public defence
2014-06-13, N420, Naturvetarhuset, Umeå, 21:36 (English)
Opponent
Supervisors
Available from: 2014-05-23 Created: 2014-05-22 Last updated: 2014-09-22Bibliographically approved
2. Toward higher-order network models
Open this publication in new window or tab >>Toward higher-order network models
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Complex systems play an essential role in our daily lives. These systems consist of many connected components that interact with each other. Consider, for example, society with billions of collaborating individuals, the stock market with numerous buyers and sellers that trade equities, or communication infrastructures with billions of phones, computers and satellites.

The key to understanding complex systems is to understand the interaction patterns between their components - their networks. To create the network, we need data from the system and a model that organizes the given data in a network representation. Today's increasing availability of data and improved computational capacity for analyzing networks have created great opportunities for the network approach to further prosper. However, increasingly rich data also gives rise to new challenges that question the effectiveness of the conventional approach to modeling data as a network. In this thesis, we explore those challenges and provide methods for simplifying and highlighting important interaction patterns in network models that make use of richer data.

Using data from real-world complex systems, we first show that conventional network modeling can provide valuable insights about the function of the underlying system. To explore the impact of using richer data in the network representation, we then expand the analysis for higher-order models of networks and show why we need to go beyond conventional models when there is data that allows us to do so. In addition, we also present a new framework for higher-order network modeling and analysis. We find that network models that capture richer data can provide more accurate representations of many real-world complex systems.

Place, publisher, year, edition, pages
Umeå: Umeå University, 2018. p. 89
Keyword
network science, complex systems, complex networks, network analysis, higher-order networks, community detection, citation networks, network modeling
National Category
Physical Sciences Other Computer and Information Science
Research subject
Physics
Identifiers
urn:nbn:se:umu:diva-147673 (URN)978-91-7601-892-7 (ISBN)
Public defence
2018-06-08, Sal N420, Naturvetarhuset, Umeå, 13:00 (English)
Opponent
Supervisors
Available from: 2018-05-18 Created: 2018-05-14 Last updated: 2018-05-15Bibliographically approved

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