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Mapping bilateral information interests using the activity of Wikipedia editors
Leibniz Institute for the Social Sciences, Cologne, Germany. (Integrated Science Laboratory)
Umeå University, Faculty of Science and Technology, Department of Physics. (Integrated Science Laboratory)
Leibniz-Institute for the Social Sciences, Cologne, Germany.
Umeå University, Faculty of Science and Technology, Department of Physics.
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2015 (English)In: Palgrave communications, ISSN 2055-1045, Vol. 1, p. 1-7, article id 15041Article in journal (Refereed) Published
Abstract [en]

We live in a global village where electronic communication has eliminated the geographical barriers of information exchange. The road is now open to worldwide convergence of information interests, shared values and understanding. Nevertheless, interests still vary between countries around the world. This raises important questions about what today’s world map of information interests actually looks like and what factors cause the barriers of information exchange between countries. To quantitatively construct a world map of information interests, we devise a scalable statistical model that identifies countries with similar information interests and measures the countries’ bilateral similarities. From the similarities we connect countries in a global network and find that countries can be mapped into 18 clusters with similar information interests. Through regression we find that language and religion best explain the strength of the bilateral ties and formation of clusters. Our findings provide a quantitative basis for further studies to better understand the complex interplay between shared interests and conflict on a global scale. The methodology can also be extended to track changes over time and capture important trends in global information exchange.

Place, publisher, year, edition, pages
2015. Vol. 1, p. 1-7, article id 15041
Keywords [en]
Information, Network, Globalization, Wikipedia
National Category
Information Systems Information Studies
Research subject
Computer and Information Science
Identifiers
URN: urn:nbn:se:umu:diva-98814DOI: 10.1057/palcomms.2015.41OAI: oai:DiVA.org:umu-98814DiVA, id: diva2:783828
Note

Originally published in manuscript form with the title: Local Interests in a Global World

Available from: 2015-01-27 Created: 2015-01-27 Last updated: 2018-06-07Bibliographically approved
In thesis
1. Tightly knit: spreading processes in empirical temporal networks
Open this publication in new window or tab >>Tightly knit: spreading processes in empirical temporal networks
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

We live in a tightly knit world. Our emotions, desires, perceptions and decisions are interlinked in our interactions with others. We are constantly influencing our surroundings and being influenced by others. In this thesis, we unfold some aspects of social and economical interactions by studying empirical datasets. We project these interactions into a network representation to gain insights on how socio-economic systems form and function and how they change over time. Specifically, this thesis is centered on four main questions: How do the means of communication shape our social network structures? How can we uncover the underlying network of interests from massive observational data? How does a crisis spread in a real financial network? How do the dynamics of interaction influence spreading processes in networks? We use a variety of methods from physics, psychology, sociology, and economics as well as computational, mathematical and statistical analysis to address these questions.

Place, publisher, year, edition, pages
Umeå: Umeå University, 2015. p. 55
National Category
Natural Sciences
Research subject
Physics
Identifiers
urn:nbn:se:umu:diva-98885 (URN)978-91-7601-209-3 (ISBN)
Public defence
2015-02-20, NC 300, Naturvetarhuset, Umeå, 12:00 (English)
Opponent
Supervisors
Available from: 2015-01-30 Created: 2015-01-27 Last updated: 2018-06-07Bibliographically approved
2. Toward higher-order network models
Open this publication in new window or tab >>Toward higher-order network models
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

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.

Place, publisher, year, edition, pages
Umeå: Umeå University, 2018. p. 89
Keywords
network science, complex systems, complex networks, network analysis, higher-order networks, community detection, citation networks, network modeling
National Category
Physical Sciences Other Computer and Information Science
Research subject
Physics
Identifiers
urn:nbn:se:umu:diva-147673 (URN)978-91-7601-892-7 (ISBN)
Public defence
2018-06-08, Sal N420, Naturvetarhuset, Umeå, 13:00 (English)
Opponent
Supervisors
Available from: 2018-05-18 Created: 2018-05-14 Last updated: 2018-06-11Bibliographically approved

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Karimi, FaribaBohlin, LudvigRosvall, MartinLancichinetti, Andrea

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