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Dynamics of interacting information waves in networks
Umeå University, Faculty of Science and Technology, Department of Physics.
Umeå University, Faculty of Science and Technology, Department of Physics.
Umeå University, Faculty of Science and Technology, Department of Physics.
Umeå University, Faculty of Science and Technology, Department of Physics.
2014 (English)In: Physical Review E. Statistical, Nonlinear, and Soft Matter Physics, ISSN 1539-3755, E-ISSN 1550-2376, Vol. 89, no 1, 012809- p.Article in journal (Refereed) Published
Abstract [en]

To better understand the inner workings of information spreading, network researchers often use simple models to capture the spreading dynamics. But most models only highlight the effect of local interactions on the global spreading of a single information wave, and ignore the effects of interactions between multiple waves. Here we take into account the effect of multiple interacting waves by using an agent-based model in which the interaction between information waves is based on their novelty. We analyzed the global effects of such interactions and found that information that actually reaches nodes reaches them faster. This effect is caused by selection between information waves: lagging waves die out and only leading waves survive. As a result, and in contrast to models with noninteracting information dynamics, the access to information decays with the distance from the source. Moreover, when we analyzed the model on various synthetic and real spatial road networks, we found that the decay rate also depends on the path redundancy and the effective dimension of the system. In general, the decay of the information wave frequency as a function of distance from the source follows a power-law distribution with an exponent between -0.2 for a two-dimensional system with high path redundancy and -0.5 for a tree-like system with no path redundancy. We found that the real spatial networks provide an infrastructure for information spreading that lies in between these two extremes. Finally, to better understand the mechanics behind the scaling results, we provide analytical calculations of the scaling for a one-dimensional system.

Place, publisher, year, edition, pages
American Physical Society , 2014. Vol. 89, no 1, 012809- p.
National Category
Physical Sciences
Identifiers
URN: urn:nbn:se:umu:diva-87423DOI: 10.1103/PhysRevE.89.012809ISI: 000332166500011OAI: oai:DiVA.org:umu-87423DiVA: diva2:709431
Available from: 2014-04-01 Created: 2014-03-31 Last updated: 2017-12-05Bibliographically 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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Mirshahvalad, AtiehViamontes Esquivel, AlcidesLizana, LudvigRosvall, Martin
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