Umeå University's logo

umu.sePublications
Change search
Link to record
Permanent link

Direct link
Alternative names
Publications (10 of 11) Show all publications
Holmgren, A., Edler, D. & Rosvall, M. (2023). Mapping change in higher-order networks with multilevel and overlapping communities. Applied Network Science, 8(1), Article ID 42.
Open this publication in new window or tab >>Mapping change in higher-order networks with multilevel and overlapping communities
2023 (English)In: Applied Network Science, E-ISSN 2364-8228, Vol. 8, no 1, article id 42Article in journal (Refereed) Published
Abstract [en]

New network models of complex systems use layers, state nodes, or hyperedges to capture higher-order interactions and dynamics. Simplifying how the higher-order networks change over time or depending on the network model would be easy with alluvial diagrams, which visualize community splits and merges between networks. However, alluvial diagrams were developed for networks with regular nodes assigned to non-overlapping flat communities. How should they be defined for nodes in layers, state nodes, or hyperedges? How can they depict multilevel, overlapping communities? Here we generalize alluvial diagrams to map change in higher-order networks and provide an interactive tool for anyone to generate alluvial diagrams. We use the alluvial diagram generator in three case studies to illustrate significant changes in the organization of science, the effect of modeling network flows with memory in a citation network and distinguishing multidisciplinary from field-specific journals, and the effects of multilayer representation of a collaboration hypergraph.

Place, publisher, year, edition, pages
Springer Nature, 2023
National Category
Computer Sciences
Identifiers
urn:nbn:se:umu:diva-212419 (URN)10.1007/s41109-023-00572-5 (DOI)001026667400001 ()2-s2.0-85165115770 (Scopus ID)
Funder
Swedish Foundation for Strategic Research, SB16-0089Swedish Research Council, 2016-00796
Available from: 2023-07-31 Created: 2023-07-31 Last updated: 2023-07-31Bibliographically approved
Holmgren, A. (2023). Mapping higher-order dynamics and interactions in complex networks. (Doctoral dissertation). Umeå: Umeå universitet
Open this publication in new window or tab >>Mapping higher-order dynamics and interactions in complex networks
2023 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Kartläggning av högre ordningens dynamik och interaktioner i komplexa nätverk
Abstract [en]

Complex systems research seeks to explain emergent properties in social, technological, and biological systems that result from interactions between their components. As data on the intricate relationships within these systems become increasingly available, there is a growing need for more sophisticated models to describe them accurately and offer deeper insights.

This thesis addresses challenges in incorporating higher-order interactions and dynamics into the analysis of complex systems that go beyond standard network approaches. It covers mapping changing network organizations, modeling higher-order dynamics on ordinary networks, integrating network structure and metadata, and modeling multibody interactions. The thesis offers new tools and models to enhance our understanding of how higher-order dynamics and interactions shape the organization and give rise to the function of complex systems by providing more accurate representations than traditional network models. These findings pave the way for new research in network science.

Abstract [sv]

Forskning om komplexa system strävar efter att förklara egenskaper som uppstår i sociala, teknologiska och biologiska system genom samspel mellan deras delar. När allt mer data om dessa relationer blir tillgänglig ökar behovet av mer avancerade modeller för att beskriva systemen korrekt och ge djupare insikter.

Den här avhandlingen tar upp utmaningar med att inkludera högre ordningens interaktioner och dynamik i analysen av komplexa system, genom att använda högre ordningens nätverksmodeller. Den behandlar kartläggning av förändrade nätverksstrukturer, modellering av högre ordningens dynamik i vanliga nätverk, kombinering av nätverksstruktur och metadata samt modellering av flerkroppsinteraktioner. De nya verktygen och modellerna ökar vår förståelse för hur högre ordningens dynamik och interaktioner påverkar organisationen och funktionen hos komplexa system. Detta görs genom att erbjuda mer precisa representationer än traditionella nätverksmodeller. Dessa resultat öppnar upp för framtida forskning inom nätverksvetenskap.

Place, publisher, year, edition, pages
Umeå: Umeå universitet, 2023. p. 65
Keywords
networks, community detection, information theory, the map equation
National Category
Other Physics Topics
Identifiers
urn:nbn:se:umu:diva-206628 (URN)978-91-7855-984-8 (ISBN)978-91-7855-985-5 (ISBN)
Public defence
2023-05-12, NAT.D.410, Naturvetarhuset, Umeå, 09:00 (English)
Opponent
Supervisors
Available from: 2023-04-21 Created: 2023-04-13 Last updated: 2023-04-14Bibliographically approved
Holmgren, A., Bernenko, D. & Lizana, L. (2023). Mapping robust multiscale communities in chromosome contact networks. Scientific Reports, 13(1), Article ID 12979.
Open this publication in new window or tab >>Mapping robust multiscale communities in chromosome contact networks
2023 (English)In: Scientific Reports, E-ISSN 2045-2322, Vol. 13, no 1, article id 12979Article in journal (Refereed) Published
Abstract [en]

To better understand DNA’s 3D folding in cell nuclei, researchers developed chromosome capture methods such as Hi-C that measure the contact frequencies between all DNA segment pairs across the genome. As Hi-C data sets often are massive, it is common to use bioinformatics methods to group DNA segments into 3D regions with correlated contact patterns, such as Topologically associated domains and A/B compartments. Recently, another research direction emerged that treats the Hi-C data as a network of 3D contacts. In this representation, one can use community detection algorithms from complex network theory that group nodes into tightly connected mesoscale communities. However, because Hi-C networks are so densely connected, several node partitions may represent feasible solutions to the community detection problem but are indistinguishable unless including other data. Because this limitation is a fundamental property of the network, this problem persists regardless of the community-finding or data-clustering method. To help remedy this problem, we developed a method that charts the solution landscape of network partitions in Hi-C data from human cells. Our approach allows us to scan seamlessly through the scales of the network and determine regimes where we can expect reliable community structures. We find that some scales are more robust than others and that strong clusters may differ significantly. Our work highlights that finding a robust community structure hinges on thoughtful algorithm design or method cross-evaluation.

Place, publisher, year, edition, pages
Springer Nature, 2023
National Category
Other Computer and Information Science Other Physics Topics
Identifiers
urn:nbn:se:umu:diva-207388 (URN)10.1038/s41598-023-39522-7 (DOI)001049382700091 ()37563218 (PubMedID)2-s2.0-85167681886 (Scopus ID)
Funder
Swedish Research Council, 2017-03848Swedish Research Council, 2021-04080Swedish Foundation for Strategic Research, SB16-0089
Note

Originally included in thesis in manuscript form. 

Available from: 2023-04-26 Created: 2023-04-26 Last updated: 2023-09-13Bibliographically approved
Eriksson, A., Carletti, T., Lambiotte, R., Rojas, A. & Rosvall, M. (2022). Flow-Based Community Detection in Hypergraphs. In: Federico Battiston; Giovanni Petri (Ed.), Higher-Order Systems: (pp. 141-161). Springer Science+Business Media B.V.
Open this publication in new window or tab >>Flow-Based Community Detection in Hypergraphs
Show others...
2022 (English)In: Higher-Order Systems / [ed] Federico Battiston; Giovanni Petri, Springer Science+Business Media B.V., 2022, , p. 21p. 141-161Chapter in book (Refereed)
Abstract [en]

To connect structure, dynamics and function in systems with multibody interactions, network scientists model random walks on hypergraphs and identify communities that confine the walks for a long time. The two flow-based community-detection methods Markov stability and the map equation identify such communities based on different principles and search algorithms. But how similar are the resulting communities? We explain both methods’ machinery applied to hypergraphs and compare them on synthetic and real-world hypergraphs using various hyperedge-size biased random walks and time scales. We find that the map equation is more sensitive to time-scale changes and that Markov stability is more sensitive to hyperedge-size biases.

Place, publisher, year, edition, pages
Springer Science+Business Media B.V., 2022. p. 21
Series
Understanding Complex Systems, ISSN 1860-0832, E-ISSN 1860-0840
National Category
Computer Sciences
Identifiers
urn:nbn:se:umu:diva-194897 (URN)10.1007/978-3-030-91374-8_4 (DOI)2-s2.0-85129151109 (Scopus ID)978-3-030-91374-8 (ISBN)
Available from: 2022-06-09 Created: 2022-06-09 Last updated: 2023-04-13Bibliographically approved
Edler, D., Holmgren, A., Rojas, A., Rosvall, M. & Antonelli, A. (2022). 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
Show others...
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
Bassolas, A., Holmgren, A., Marot, A., Rosvall, M. & Nicosia, V. (2022). Mapping nonlocal relationships between metadata and network structure with metadata-dependent encoding of random walks. Science Advances, 8(43), Article ID eabn7558.
Open this publication in new window or tab >>Mapping nonlocal relationships between metadata and network structure with metadata-dependent encoding of random walks
Show others...
2022 (English)In: Science Advances, E-ISSN 2375-2548, Vol. 8, no 43, article id eabn7558Article in journal (Refereed) Published
Abstract [en]

Integrating structural information and metadata, such as gender, social status, or interests, enriches networks and enables a better understanding of the large-scale structure of complex systems. However, existing approaches to augment networks with metadata for community detection only consider immediately adjacent nodes and cannot exploit the nonlocal relationships between metadata and large-scale network structure present in many spatial and social systems. Here, we develop a flow-based community detection framework based on the map equation that integrates network information and metadata of distant nodes and reveals more complex relationships. We analyze social and spatial networks and find that our methodology can detect functional metadata-informed communities distinct from those derived solely from network information or metadata. For example, in a mobility network of London, we identify communities that reflect the heterogeneity of income distribution, and in a European power grid network, we identify communities that capture relationships between geography and energy prices beyond country borders.

Place, publisher, year, edition, pages
American Association for the Advancement of Science (AAAS), 2022
National Category
Computer Sciences
Identifiers
urn:nbn:se:umu:diva-200999 (URN)10.1126/sciadv.abn7558 (DOI)000890263700001 ()36306360 (PubMedID)2-s2.0-85141005553 (Scopus ID)
Available from: 2022-11-16 Created: 2022-11-16 Last updated: 2023-09-05Bibliographically approved
Eriksson, A., Edler, D., Rojas, A., de Domenico, M. & Rosvall, M. (2021). How choosing random-walk model and network representation matters for flow-based community detection in hypergraphs. Communications Physics, 4(1), Article ID 133.
Open this publication in new window or tab >>How choosing random-walk model and network representation matters for flow-based community detection in hypergraphs
Show others...
2021 (English)In: Communications Physics, E-ISSN 2399-3650, Vol. 4, no 1, article id 133Article in journal (Refereed) Published
Abstract [en]

Hypergraphs offer an explicit formalism to describe multibody interactions in complex systems. To connect dynamics and function in systems with these higher-order interactions, network scientists have generalised random-walk models to hypergraphs and studied the multibody effects on flow-based centrality measures. Mapping the large-scale structure of those flows requires effective community detection methods applied to cogent network representations. For different hypergraph data and research questions, which combination of random-walk model and network representation is best? We define unipartite, bipartite, and multilayer network representations of hypergraph flows and explore how they and the underlying random-walk model change the number, size, depth, and overlap of identified multilevel communities. These results help researchers choose the appropriate modelling approach when mapping flows on hypergraphs.

Place, publisher, year, edition, pages
Nature Publishing Group, 2021
National Category
Computer Sciences
Identifiers
urn:nbn:se:umu:diva-184903 (URN)10.1038/s42005-021-00634-z (DOI)000663511200004 ()2-s2.0-85107742703 (Scopus ID)
Available from: 2021-06-22 Created: 2021-06-22 Last updated: 2023-04-14Bibliographically approved
Calatayud, J., Neuman, M., Rojas, A., Eriksson, A. & Rosvall, M. (2021). Regularities in species’ niches reveal the world’s climate regions. eLIFE, 10, Article ID e58397.
Open this publication in new window or tab >>Regularities in species’ niches reveal the world’s climate regions
Show others...
2021 (English)In: eLIFE, E-ISSN 2050-084X, Vol. 10, article id e58397Article in journal (Refereed) Published
Abstract [en]

Climate regions form the basis of many ecological, evolutionary, and conservation studies. However, our understanding of climate regions is limited to how they shape vegetation: They do not account for the distribution of animals. Here we develop a network-based framework to identify important climates worldwide based on regularities in realized niches of about 26,000 tetrapods. We show that high-energy climates, including deserts, tropical savannas, and steppes, are consistent across animal-and plant-derived classifications, indicating similar underlying climatic determinants. Conversely, temperate climates differ across all groups, suggesting that these climates allow for idiosyncratic adaptations. Finally, we show how the integration of niche classifications with geographical information enables the detection of climatic transition zones and the signal of geographic and historical processes. Our results identify the climates shaping the distribution of tetrapods and call for caution when using general climate classifications to study the ecology, evolution, or conservation of specific taxa.

Place, publisher, year, edition, pages
eLife Sciences Publications Ltd., 2021
National Category
Climate Research
Identifiers
urn:nbn:se:umu:diva-181691 (URN)10.7554/eLife.58397 (DOI)000630186300001 ()2-s2.0-85101908250 (Scopus ID)
Available from: 2021-03-23 Created: 2021-03-23 Last updated: 2023-09-05Bibliographically approved
Holmgren, A., Blöcker, C. & Rosvall, M.Mapping biased higher-order walks reveals overlapping communities.
Open this publication in new window or tab >>Mapping biased higher-order walks reveals overlapping communities
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Researchers use networks to model relational data from complex systems, and tools from network science to map them and understand their function. Flow communities capture the organization of various real-world systems such as social networks, protein-protein interactions, and species distributions, and are often overlapping. However, mapping overlapping flow-based communities requires higher-order data, which is not always available. To address this issue, we take inspiration from the representation-learning algorithm node2vec, and apply higher-order biased random walks on first-order networks to obtain higher-order data. But instead of explicitly simulating the walks, we model them with sparse memory networks and control the complexity of the higher-order model with an information-theoretic approach through a tunable information-loss parameter. Using the map equation framework, we partition the resulting higher-order networks into overlapping modules. We find that our method recovers planted overlapping partitions in synthetic benchmarks and identifies overlapping communities in real-world networks.

Keywords
networks, community detection, information theory, the map equation
National Category
Other Physics Topics
Identifiers
urn:nbn:se:umu:diva-206627 (URN)10.48550/arXiv.2304.05775 (DOI)
Available from: 2023-04-13 Created: 2023-04-13 Last updated: 2023-04-13
Holmgren, A., Edler, D. & Rosvall, M.Mapping change in higher-order networks with multilevel and overlapping communities.
Open this publication in new window or tab >>Mapping change in higher-order networks with multilevel and overlapping communities
(English)Manuscript (preprint) (Other academic)
Abstract [en]

New network models of complex systems use layers, state nodes, or hyperedges to capture higher-order interactions and dynamics. Simplifying how the higher-order networks change over time or depending on the network model would be easy with alluvial diagrams, which visualize community splits and merges between networks. However, alluvial diagrams were developed for networks with regular nodes assigned to non-overlapping flat communities. How should they be defined for nodes in layers, state nodes, or hyperedges? How can they depict multilevel, overlapping communities? Here we generalize alluvial diagrams to map change in higher-order networks and provide an interactive tool for anyone to generate alluvial diagrams. We use the alluvial generator to illustrate the effect of modeling network flows with memory in a citation network, distinguishing multidisciplinary from field-specific journals. 

Keywords
networks, community detection, the map equation
National Category
Other Physics Topics
Identifiers
urn:nbn:se:umu:diva-206626 (URN)10.48550/arXiv.2303.00622 (DOI)
Available from: 2023-04-13 Created: 2023-04-13 Last updated: 2023-04-13
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-5859-4073

Search in DiVA

Show all publications