Toward higher-order network models

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Bohlin, Ludvig,1986-Umeå universitet,Institutionen för fysik (author)

Toward higher-order network models

BookEnglish2018

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Umeå :Umeå University,2018
89 s.
electronicrdacarrier

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LIBRIS-ID:oai:DiVA.org:umu-147673
ISBN:9789176018927
https://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-147673URI

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Language:English
Summary in:English

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SwePubSwePub

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Subject category:vet swepub-contenttype
Subject category:dok swepub-publicationtype

Notes

Complex systems play an essential role in our daily lives. These systems consist of many connected components that interact with each other. Consider, for example, society with billions of collaborating individuals, the stock market with numerous buyers and sellers that trade equities, or communication infrastructures with billions of phones, computers and satellites.The key to understanding complex systems is to understand the interaction patterns between their components - their networks. To create the network, we need data from the system and a model that organizes the given data in a network representation. Today's increasing availability of data and improved computational capacity for analyzing networks have created great opportunities for the network approach to further prosper. However, increasingly rich data also gives rise to new challenges that question the effectiveness of the conventional approach to modeling data as a network. In this thesis, we explore those challenges and provide methods for simplifying and highlighting important interaction patterns in network models that make use of richer data.Using data from real-world complex systems, we first show that conventional network modeling can provide valuable insights about the function of the underlying system. To explore the impact of using richer data in the network representation, we then expand the analysis for higher-order models of networks and show why we need to go beyond conventional models when there is data that allows us to do so. In addition, we also present a new framework for higher-order network modeling and analysis. We find that network models that capture richer data can provide more accurate representations of many real-world complex systems.

Subject headings and genre

NATURVETENSKAP Fysik hsv//swe
NATURAL SCIENCES Physical Sciences hsv//eng
NATURVETENSKAP Data- och informationsvetenskap Annan data- och informationsvetenskap hsv//swe
NATURAL SCIENCES Computer and Information Sciences Other Computer and Information Science hsv//eng
network science
complex systems
complex networks
network analysis
higher-order networks
community detection
citation networks
network modeling
fysik
Physics

Added entries (persons, corporate bodies, meetings, titles ...)

Rosvall, Martin,UniversitetslektorUmeå universitet,Institutionen för fysik(Swepub:umu)maro0001 (thesis advisor)
Lizana, Ludvig,UniversitetslektorUmeå universitet,Institutionen för fysik(Swepub:umu)luli0012 (thesis advisor)
Eliassi-Rad, Tina,Associate ProfessorNetwork Science Institute & College of Computer and Information Science, Northeastern University, Boston, USA (opponent)
Umeå universitetInstitutionen för fysik (creator_code:org_t)

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