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Mapping flows on sparse networks with missing links
Umeå University, Faculty of Science and Technology, Department of Physics. Scientific Computing Laboratory, Center for the Study of Complex Systems, Institute of Physics, University of Belgrade, Serbia.ORCID iD: 0000-0003-0124-1909
Umeå University, Faculty of Science and Technology, Department of Physics. Gothenburg Global Biodiversity Centre, Gothenburg, Sweden; Department of Biological and Environmental Sciences, University of Gothenburg, Sweden.ORCID iD: 0000-0001-5420-0591
Umeå University, Faculty of Science and Technology, Department of Physics.ORCID iD: 0000-0002-7181-9940
2020 (English)In: Physical review. E, ISSN 2470-0045, E-ISSN 2470-0053, Vol. 102, no 1, article id 012302Article in journal (Refereed) Published
Abstract [en]

Unreliable network data can cause community-detection methods to overfit and highlight spurious structures with misleading information about the organization and function of complex systems. Here we show how to detect significant flow-based communities in sparse networks with missing links using the map equation. Since the map equation builds on Shannon entropy estimation, it assumes complete data such that analyzing undersampled networks can lead to overfitting. To overcome this problem, we incorporate a Bayesian approach with assumptions about network uncertainties into the map equation framework. Results in both synthetic and real-world networks show that the Bayesian estimate of the map equation provides a principled approach to revealing significant structures in undersampled networks.

Place, publisher, year, edition, pages
American Physical Society, 2020. Vol. 102, no 1, article id 012302
National Category
Computer Sciences Other Physics Topics
Identifiers
URN: urn:nbn:se:umu:diva-173895DOI: 10.1103/PhysRevE.102.012302ISI: 000550381200011Scopus ID: 2-s2.0-85089465455OAI: oai:DiVA.org:umu-173895DiVA, id: diva2:1456632
Funder
Swedish Research Council, 2016-00796Available from: 2020-08-06 Created: 2020-08-06 Last updated: 2023-03-24Bibliographically approved
In thesis
1. Mapping incomplete relational data: networks in ecology & evolution
Open this publication in new window or tab >>Mapping incomplete relational data: networks in ecology & evolution
2022 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Kartläggning av inkomplett relationell data : nätverk inom ekologi & evolution
Abstract [en]

We live in an interconnected world full of complex systems that cannot be understood simply by analyzing their components. From how genes regulate biological functions to the distribution of life on Earth, we need methods that can analyze systems as a whole.

Networks are abstractions of complex systems, helping capture properties that emerge from patterns of interactions rather than from the individual parts. To understand the patterns of interactions in large networks, we need to simplify them by discovering their modular structure that often characterizes complex systems. A hierarchical modular structure functions as a map that lets us navigate relational data efficiently and helps us see the general patterns. But how reliable is the map if it is based on incomplete data?

This thesis applies and builds upon the map equation, which is an information-theoretic method for detecting modular regularities in the flow patterns on networks. To robustly map incomplete data, we have developed three general approaches: (1) Adaptive resolution in both sampling of and dynamics on networks better fits the data. (2) Regularization avoids overfitting to random patterns. (3) Richer data can be included into the network for a more complete map. Methods that can include evolutionary relationships and handle incomplete data provide more powerful tools for mapping biodiversity in space and time.

Abstract [sv]

Vi lever i en sammankopplad värld full av komplexa system som inte låter sig förstås enbart genom att analysera dess komponenter. Från hur gener reglerar biologiska funktioner till livets utbredning på jorden behöver vi metoder som kan analysera system som en helhet.

Nätverk är abstraktioner av komplexa system som hjälper till att fånga egenskaper som uppstår genom interaktionsmönster snarare än hos de enskilda delarna. För att förstå dessa mönster i stora nätverk måste vi förenkla dem genom att upptäcka dess modulära stuktur som präglar komplexa system. En hierarkisk modulär struktur fungerar som en karta som låter oss navigera effektivt i relationsdata och hjälper oss att se de allmänna mönstren. Men hur tillförlitlig är kartan om den baseras på inkompletta data?

Den här avhandlingen applicerar och bygger vidare på kartekvationen som är en informationsteoretisk metod för att upptäcka modulära regelbundenheter i flödesmönstren på nätverk.För att robust kartlägga inkompletta data har vi utvecklat tre övergripande tillvägagångssätt: (1) Adaptiv upplösning i båda sampling av och dynamik på nätverk ger bättre anpassning till data. (2) Regularisering undviker överanpassning till slumpmässiga mönster. (3) Rikare data kan inkluderas i nätverket för en mer komplett karta. Metoder som kan inkludera evolutionära relationer och hantera inkompletta data ger kraftfullare verktyg för att kartlägga den biologiska mångfalden i rum och tid.

Place, publisher, year, edition, pages
Umeå: Umeå University, 2022. p. 66
Keywords
network science, information theory, map equation, community detection, biogeography, evolution
National Category
Computer Sciences Other Physics Topics Biological Systematics
Identifiers
urn:nbn:se:umu:diva-201176 (URN)978-91-7855-887-2 (ISBN)978-91-7855-888-9 (ISBN)
Public defence
2022-12-19, NAT.D.410, Naturvetarhuset, Umeå, 09:00 (English)
Opponent
Supervisors
Available from: 2022-11-28 Created: 2022-11-22 Last updated: 2022-11-24Bibliographically approved

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Smiljanic, JelenaEdler, DanielRosvall, Martin

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