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Toward higher-order network models
Umeå University, Faculty of Science and Technology, Department of Physics.
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 [en]
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: urn:nbn:se:umu:diva-147673ISBN: 978-91-7601-892-7 (print)OAI: oai:DiVA.org:umu-147673DiVA, id: diva2:1205422
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
List of papers
1. Stock Portfolio Structure of Individual Investors Infers Future Trading Behavior
Open this publication in new window or tab >>Stock Portfolio Structure of Individual Investors Infers Future Trading Behavior
2014 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 9, no 7, p. e103006-Article in journal (Refereed) Published
Abstract [en]

Although the understanding of and motivation behind individual trading behavior is an important puzzle in finance, little is known about the connection between an investor's portfolio structure and her trading behavior in practice. In this paper, we investigate the relation between what stocks investors hold, and what stocks they buy, and show that investors with similar portfolio structures to a great extent trade in a similar way. With data from the central register of shareholdings in Sweden, we model the market in a similarity network, by considering investors as nodes, connected with links representing portfolio similarity. From the network, we find investor groups that not only identify different investment strategies, but also represent individual investors trading in a similar way. These findings suggest that the stock portfolios of investors hold meaningful information, which could be used to earn a better understanding of stock market dynamics.

Place, publisher, year, edition, pages
plos one, 2014
National Category
Physical Sciences
Identifiers
urn:nbn:se:umu:diva-92943 (URN)10.1371/journal.pone.0103006 (DOI)000339993700029 ()
Available from: 2014-09-17 Created: 2014-09-09 Last updated: 2018-06-07Bibliographically approved
2. Mapping bilateral information interests using the activity of Wikipedia editors
Open this publication in new window or tab >>Mapping bilateral information interests using the activity of Wikipedia editors
Show others...
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.

Keywords
Information, Network, Globalization, Wikipedia
National Category
Information Systems Information Studies
Research subject
Computer and Information Science
Identifiers
urn:nbn:se:umu:diva-98814 (URN)10.1057/palcomms.2015.41 (DOI)
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
3. Robustness of journal rankings by network flows with different amounts of memory
Open this publication in new window or tab >>Robustness of journal rankings by network flows with different amounts of memory
2016 (English)In: Journal of the Association for Information Science and Technology, ISSN 2330-1635, E-ISSN 2330-1643, Vol. 67, no 10, p. 2527-2535Article in journal (Refereed) Published
Abstract [en]

As the number of scientific journals has multiplied, journal rankings have become increasingly important for scientific decisions. From submissions and subscriptions to grants and hirings, researchers, policy makers, and funding agencies make important decisions influenced by journal rankings such as the ISI journal impact factor. Typically, the rankings are derived from the citation network between a selection of journals and unavoidably depend on this selection. However, little is known about how robust rankings are to the selection of included journals. We compare the robustness of three journal rankings based on network flows induced on citation networks. They model pathways of researchers navigating the scholarly literature, stepping between journals and remembering their previous steps to different degrees: zero-step memory as impact factor, one-step memory as Eigenfactor, and two-step memory, corresponding to zero-, first-, and second-order Markov models of citation flow between journals. We conclude that higher-order Markov models perform better and are more robust to the selection of journals. Whereas our analysis indicates that higher-order models perform better, the performance gain for higher-order Markov models comes at the cost of requiring more citation data over a longer time period.

National Category
Other Physics Topics Information Studies
Research subject
Physics
Identifiers
urn:nbn:se:umu:diva-89147 (URN)10.1002/asi.23582 (DOI)000384509100016 ()
Note

Originally published in thesis in manuscript form.

Available from: 2014-05-22 Created: 2014-05-22 Last updated: 2018-06-07Bibliographically approved
4. 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, ISSN 1999-4893, 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
Identifiers
urn:nbn:se:umu:diva-144114 (URN)10.3390/a10040112 (DOI)000419169400004 ()
Available from: 2018-01-26 Created: 2018-01-26 Last updated: 2018-06-09Bibliographically approved
5. Maps of sparse Markov chains efficiently reveal community structure in network flows with memory
Open this publication in new window or tab >>Maps of sparse Markov chains efficiently reveal community structure in network flows with memory
(English)Manuscript (preprint) (Other academic)
Abstract [en]

To better understand the flows of ideas or information through social and biological systems, researchers develop maps that reveal important patterns in network flows. In practice, network flow models have implied memoryless first-order Markov chains, but recently researchers have introduced higher-order Markov chain models with memory to capture patterns in multi-step pathways. Higher-order models are particularly important for effectively revealing actual, overlapping community structure, but higher-order Markov chain models suffer from the curse of dimensionality: their vast parameter spaces require exponentially increasing data to avoid overfitting and therefore make mapping inefficient already for moderate-sized systems. To overcome this problem, we introduce an efficient cross-validated mapping approach based on network flows modeled by sparse Markov chains. To illustrate our approach, we present a map of citation flows in science with research fields that overlap in multidisciplinary journals. Compared with currently used categories in science of science studies, the research fields form better units of analysis because the map more effectively captures how ideas flow through science.

Keywords
higher-order networks, sparse memory networks
National Category
Physical Sciences
Identifiers
urn:nbn:se:umu:diva-147658 (URN)
Available from: 2018-05-14 Created: 2018-05-14 Last updated: 2018-06-09

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