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Memory in network flows and its effects on community detection, ranking, and spreading
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)
Umeå University, Faculty of Science and Technology, Department of Physics. (Icelab)
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(English)Manuscript (preprint) (Other academic)
Abstract [en]

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.

National Category
Other Physics Topics
Research subject
Physics
Identifiers
URN: urn:nbn:se:umu:diva-89146OAI: oai:DiVA.org:umu-89146DiVA: diva2:719090
Available from: 2014-05-22 Created: 2014-05-22 Last updated: 2014-09-22Bibliographically 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. 62 p.
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

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Rosvall, MartinViamontes Esquivel, AlcidesWest, JevinLancichinetti, Andrea
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