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Compression of Flow Can Reveal Overlapping-Module Organization in Networks
Umeå University, Faculty of Science and Technology, Department of Physics. (IceLab)
Umeå University, Faculty of Science and Technology, Department of Physics. (IceLab)
2011 (English)In: Physical Review X, ISSN 2160-3308, Vol. 1, no 2, 021025Article in journal (Refereed) Published
Abstract [en]

To better understand the organization of overlapping modules in large networks with respect to flow, we introduce the map equation for overlapping modules. In this information-theoretic framework, we use the correspondence between compression and regularity detection. The generalized map equation measures how well we can compress a description of flow in the network when we partition it into modules with possible overlaps. When we minimize the generalized map equation over overlapping network partitions, we detect modules that capture flow and determine which nodes at the boundaries between modules should be classified in multiple modules and to what degree. With a novel greedy-search algorithm, we find that some networks, for example, the neural network of the nematode Caenorhabditis elegans, are best described by modules dominated by hard boundaries, but that others, for example, the sparse European-roads network, have an organization of highly overlapping modules.

Place, publisher, year, edition, pages
College Park, Md.: American Physical Society , 2011. Vol. 1, no 2, 021025
National Category
Condensed Matter Physics
URN: urn:nbn:se:umu:diva-51246DOI: 10.1103/PhysRevX.1.021025ISI: 000310508700001OAI: diva2:477960
Swedish Research Council, 2009-5344
Available from: 2012-01-16 Created: 2012-01-15 Last updated: 2015-03-12Bibliographically 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.
complex systems, network science, community detection, model selection, signficance analysis, ergodicity
National Category
Physical Sciences
Research subject
urn:nbn:se:umu:diva-89149 (URN)978-91-7601-085-3 (ISBN)
Public defence
2014-06-13, N420, Naturvetarhuset, Umeå, 21:36 (English)
Available from: 2014-05-23 Created: 2014-05-22 Last updated: 2014-09-22Bibliographically approved

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Viamontes Esquivel, AlcidesRosvall, Martin
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