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Mapping incomplete relational data: networks in ecology & evolution
Umeå University, Faculty of Science and Technology, Department of Physics.ORCID iD: 0000-0001-5420-0591
2022 (English)Doctoral thesis, comprehensive summary (Other academic)Alternative title
Kartläggning av inkomplett relationell data : nätverk inom ekologi & evolution (Swedish)
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 [en]
network science, information theory, map equation, community detection, biogeography, evolution
National Category
Computer Sciences Other Physics Topics Biological Systematics
Identifiers
URN: urn:nbn:se:umu:diva-201176ISBN: 978-91-7855-887-2 (print)ISBN: 978-91-7855-888-9 (electronic)OAI: oai:DiVA.org:umu-201176DiVA, id: diva2:1712846
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
List of papers
1. Infomap Bioregions: Interactive Mapping of Biogeographical Regions from Species Distributions
Open this publication in new window or tab >>Infomap Bioregions: Interactive Mapping of Biogeographical Regions from Species Distributions
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2017 (English)In: Systematic Biology, ISSN 1063-5157, E-ISSN 1076-836X, Vol. 66, no 2, p. 197-204Article in journal (Refereed) Published
Abstract [en]

Biogeographical regions (bioregions) reveal how different sets of species are spatially grouped and therefore are important units for conservation, historical biogeography, ecology, and evolution. Several methods have been developed to identify bioregions based on species distribution data rather than expert opinion. One approach successfully applies network theory to simplify and highlight the underlying structure in species distributions. However, this method lacks tools for simple and efficient analysis. Here, we present Infomap Bioregions, an interactive web application that inputs species distribution data and generates bioregion maps. Species distributions may be provided as georeferenced point occurrences or range maps, and can be of local, regional, or global scale. The application uses a novel adaptive resolution method to make best use of often incomplete species distribution data. The results can be downloaded as vector graphics, shapefiles, or in table format. We validate the tool by processing large data sets of publicly available species distribution data of the world's amphibians using species ranges, and mammals using point occurrences. We then calculate the fit between the inferred bioregions and WWF ecoregions. As examples of applications, researchers can reconstruct ancestral ranges in historical biogeography or identify indicator species for targeted conservation.

Keywords
Biogeography, bioregionalization, conservation, mapping
National Category
Biological Systematics Other Physics Topics
Identifiers
urn:nbn:se:umu:diva-133791 (URN)10.1093/sysbio/syw087 (DOI)000397703800007 ()27694311 (PubMedID)2-s2.0-85018939282 (Scopus ID)
Available from: 2017-04-24 Created: 2017-04-24 Last updated: 2023-03-24Bibliographically approved
2. Mapping Higher-Order Network Flows in Memory and Multilayer Networks with Infomap
Open this publication in new window or tab >>Mapping Higher-Order Network Flows in Memory and Multilayer Networks with Infomap
2017 (English)In: Algorithms, E-ISSN 1999-4893, Vol. 10, no 4, article id 112Article in journal (Refereed) Published
Abstract [en]

Comprehending complex systems by simplifying and highlighting important dynamical patterns requires modeling and mapping higher-order network flows. However, complex systems come in many forms and demand a range of representations, including memory and multilayer networks, which in turn call for versatile community-detection algorithms to reveal important modular regularities in the flows. Here we show that various forms of higher-order network flows can be represented in a unified way with networks that distinguish physical nodes for representing a complex system's objects from state nodes for describing flows between the objects. Moreover, these so-called sparse memory networks allow the information-theoretic community detection method known as the map equation to identify overlapping and nested flow modules in data from a range of different higher-order interactions such as multistep, multi-source, and temporal data. We derive the map equation applied to sparse memory networks and describe its search algorithm Infomap, which can exploit the flexibility of sparse memory networks. Together they provide a general solution to reveal overlapping modular patterns in higher-order flows through complex systems.

Keywords
community detection, Infomap, higher-order network flows, overlapping communities, multilayer tworks, memory networks
National Category
Computer Sciences Other Physics Topics
Identifiers
urn:nbn:se:umu:diva-144114 (URN)10.3390/a10040112 (DOI)000419169400004 ()2-s2.0-85038629313 (Scopus ID)
Available from: 2018-01-26 Created: 2018-01-26 Last updated: 2023-03-29Bibliographically approved
3. Mapping flows on sparse networks with missing links
Open this publication in new window or tab >>Mapping flows on sparse networks with missing links
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
National Category
Computer Sciences Other Physics Topics
Identifiers
urn:nbn:se:umu:diva-173895 (URN)10.1103/PhysRevE.102.012302 (DOI)000550381200011 ()2-s2.0-85089465455 (Scopus ID)
Funder
Swedish Research Council, 2016-00796
Available from: 2020-08-06 Created: 2020-08-06 Last updated: 2023-03-24Bibliographically approved
4. Mapping flows on weighted and directed networks with incomplete observations
Open this publication in new window or tab >>Mapping flows on weighted and directed networks with incomplete observations
2021 (English)In: Journal of Complex Networks, ISSN 2051-1310, E-ISSN 2051-1329, Vol. 9, no 6, article id cnab044Article in journal (Refereed) Published
Abstract [en]

Detecting significant community structure in networks with incomplete observations is challenging because the evidence for specific solutions fades away with missing data. For example, recent research shows that flow-based community detection methods can highlight spurious communities in sparse undirected and unweighted networks with missing links. Current Bayesian approaches developed to overcome this problem do not work for incomplete observations in weighted and directed networks that describe network flows. To overcome this gap, we extend the idea behind the Bayesian estimate of the map equation for unweighted and undirected networks to enable more robust community detection in weighted and directed networks. We derive an empirical Bayes estimate of the transitions rates that can incorporate metadata information and show how an efficient implementation in the community-detection method Infomap provides more reliable communities even with a significant fraction of data missing.

Place, publisher, year, edition, pages
Oxford University Press, 2021
Keywords
community detection, directed and weighted networks, incomplete data, the map equation
National Category
Other Physics Topics
Identifiers
urn:nbn:se:umu:diva-194470 (URN)10.1093/comnet/cnab044 (DOI)000797304300006 ()2-s2.0-85128774619 (Scopus ID)
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)Knut and Alice Wallenberg FoundationSwedish Research Council, 2016-00796
Note

Errata: "Correction to “Mapping flows on weighted and directed networks with incomplete observations”, Journal of Complex Networks, Volume 10, Issue 2, April 2022, cnac010, https://doi.org/10.1093/comnet/cnac010"

Available from: 2022-05-06 Created: 2022-05-06 Last updated: 2022-12-08Bibliographically approved
5. Infomap Bioregions 2: exploring the interplay between biogeography and evolution
Open this publication in new window or tab >>Infomap Bioregions 2: exploring the interplay between biogeography and evolution
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2022 (English)Manuscript (preprint) (Other academic)
Keywords
Biogeography, bioregionalization, conservation, mapping, evolution
National Category
Biological Systematics Other Physics Topics
Identifiers
urn:nbn:se:umu:diva-201175 (URN)
Note

This is a draft for a thesis.

Available from: 2022-11-22 Created: 2022-11-22 Last updated: 2022-11-23
6. Variable Markov dynamics as a multifocal lens to map multiscale complex networks
Open this publication in new window or tab >>Variable Markov dynamics as a multifocal lens to map multiscale complex networks
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(English)Manuscript (preprint) (Other academic)
Keywords
network science, community detection, Infomap
National Category
Computer Sciences Other Physics Topics
Identifiers
urn:nbn:se:umu:diva-201174 (URN)
Available from: 2022-11-22 Created: 2022-11-22 Last updated: 2022-11-23

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Edler, Daniel

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