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Organization of information pathways in complex networks
Umeå University, Faculty of Science and Technology, Department of Physics.ORCID iD: 0000-0001-8906-2352
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

A shuman beings, we are continuously struggling to comprehend the mechanism of dierent natural systems. Many times, we face a complex system where the emergent properties of the system at a global level can not be explained by a simple aggregation of the system's components at the micro-level. To better understand the macroscopic system eects, we try to model microscopic events and their interactions. In order to do so, we rely on specialized tools to connect local mechanisms with global phenomena. One such tool is network theory. Networks provide a powerful way of modeling and analyzing complex systems based on interacting elements. The interaction pattern links the elements of the system together and provides a structure that controls how information permeates throughout the system. For example, the passing of information about job opportunities in a society depends on how social ties are organized. The interaction pattern, therefore, often is essential for reconstructing and understanding the global-scale properties of the system.

In this thesis, I describe tools and models of network theory that we use and develop to analyze the organization of social or transportation systems. More specifically, we explore complex networks by asking two general questions: First, which mechanistic theoretical models can better explain network formation or spreading processes on networks? And second, what are the signi cant functional units of real networks? For modeling, for example, we introduce a simple agent-based model that considers interacting agents in dynamic networks that in the quest for information generate groups. With the model, we found that the network and the agents' perception are interchangeable; the global network structure and the local information pathways are so entangled that one can be recovered from the other one. For investigating signi cant functional units of a system, we detect, model, and analyze signi cant communities of the network. Previously introduced methods of significance analysis suer from oversimpli ed sampling schemes. We have remedied their shortcomings by proposing two dierent approaches: rst by introducing link prediction and second by using more data when they are available. With link prediction, we can detect statistically signi cant communities in large sparse networks. We test this method on real networks, the sparse network of the European Court of Justice case law, for example, to detect signi cant and insigni cant areas of law. In the presence of large data, on the other hand, we can investigate how underlying assumptions of each method aect the results of the signi cance analysis. We used this approach to investigate dierent methods for detecting signi cant communities of time-evolving networks. We found that, when we highlight and summarize important structural changes in a network, the methods that maintain more dependencies in signi cance analysis can predict structural changes earlier.

In summary, we have tried to model the systems with as simple rules as possible to better understand the global properties of the system. We always found that maintaing information about the network structure is essential for explaining important phenomena on the global scale. We conclude that the interaction pattern between interconnected units, the network, is crucial for understanding the global behavior of complex systems because it keeps the system integrated. And remember, everything is connected, albeit not always directly.

Place, publisher, year, edition, pages
Umeå, Sweden: Umeå University , 2013. , 55 p.
Keyword [en]
Complex systems, Complex Networks, Information, Communication, Community Detection, Significance Analysis, Resampling, Information Spreading.
National Category
Computer and Information Science
Research subject
Computer and Information Science
Identifiers
URN: urn:nbn:se:umu:diva-79734ISBN: 978-91-7459-715-8 (print)OAI: oai:DiVA.org:umu-79734DiVA: diva2:644264
Public defence
2013-09-20, Naturvetarhuset, N300, Umeå universitet, Umeå, 14:00
Opponent
Supervisors
Available from: 2013-08-30 Created: 2013-08-29 Last updated: 2013-08-30Bibliographically approved
List of papers
1. Reinforced communication and social navigation: Remember your friends and remember yourself
Open this publication in new window or tab >>Reinforced communication and social navigation: Remember your friends and remember yourself
2011 (English)In: Physical Review E. Statistical, Nonlinear, and Soft Matter Physics, ISSN 1539-3755, E-ISSN 1550-2376, Vol. 84, no 3, 036102- p.Article in journal (Refereed) Published
Abstract [en]

In social systems, people communicate with each other and form groups based on their interests. The pattern of interactions, the network, and the ideas that flow on the network naturally evolve together. Researchers use simple models to capture the feedback between changing network patterns and ideas on the network, but little is understood about the role of past events in the feedback process. Here, we introduce a simple agent-based model to study the coupling between peoples' ideas and social networks, and better understand the role of history in dynamic social networks. We measure how information about ideas can be recovered from information about network structure and, the other way around, how information about network structure can be recovered from information about ideas. We find that it is, in general, easier to recover ideas from the network structure than vice versa.

Place, publisher, year, edition, pages
Melville, N.Y.: Published by the American Physical Society through the American Institute of Physics, 2011
National Category
Physical Sciences
Identifiers
urn:nbn:se:umu:diva-47906 (URN)10.1103/PhysRevE.84.036102 (DOI)000294945500001 ()
Available from: 2011-10-04 Created: 2011-10-03 Last updated: 2017-12-08Bibliographically approved
2. Significant Communities in Large Sparse Networks
Open this publication in new window or tab >>Significant Communities in Large Sparse Networks
2012 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 7, no 3, e33721- p.Article in journal (Refereed) Published
Abstract [en]

Researchers use community-detection algorithms to reveal large-scale organization in biological and social networks, but community detection is useful only if the communities are significant and not a result of noisy data. To assess the statistical significance of the network communities, or the robustness of the detected structure, one approach is to perturb the network structure by removing links and measure how much the communities change. However, perturbing sparse networks is challenging because they are inherently sensitive; they shatter easily if links are removed. Here we propose a simple method to perturb sparse networks and assess the significance of their communities. We generate resampled networks by adding extra links based on local information, then we aggregate the information from multiple resampled networks to find a coarse-grained description of significant clusters. In addition to testing our method on benchmark networks, we use our method on the sparse network of the European Court of Justice (ECJ) case law, to detect significant and insignificant areas of law. We use our significance analysis to draw a map of the ECJ case law network that reveals the relations between the areas of law.

Place, publisher, year, edition, pages
San Francisco: Public Library of Science, 2012
Keyword
Networks, Organization, Significance analysis, European Law
National Category
Other Physics Topics Law and Society Probability Theory and Statistics
Research subject
International Law
Identifiers
urn:nbn:se:umu:diva-54100 (URN)10.1371/journal.pone.0033721 (DOI)000305339100067 ()
Funder
Swedish Research Council, 2009-5344
Available from: 2012-04-17 Created: 2012-04-17 Last updated: 2017-12-07Bibliographically approved
3. Resampling effects on significance analysis of network clustering and ranking
Open this publication in new window or tab >>Resampling effects on significance analysis of network clustering and ranking
2013 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 8, no 1, e53943- p.Article in journal (Refereed) Published
Abstract [en]

Community detection helps us simplify the complex configuration of networks, but communities are reliable only if they are statistically significant. To detect statistically significant communities, a common approach is to resample the original network and analyze the communities. But resampling assumes independence between samples, while the components of a network are inherently dependent. Therefore, we must understand how breaking dependencies between resampled components affects the results of the significance analysis. Here we use scientific communication as a model system to analyze this effect. Our dataset includes citations among articles published in journals in the years 1984–2010. We compare parametric resampling of citations with non-parametric article resampling. While citation resampling breaks link dependencies, article resampling maintains such dependencies. We find that citation resampling underestimates the variance of link weights. Moreover, this underestimation explains most of the differences in the significance analysis of ranking and clustering. Therefore, when only link weights are available and article resampling is not an option, we suggest a simple parametric resampling scheme that generates link-weight variances close to the link-weight variances of article resampling. Nevertheless, when we highlight and summarize important structural changes in science, the more dependencies we can maintain in the resampling scheme, the earlier we can predict structural change. 

National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:umu:diva-64527 (URN)10.1371/journal.pone.0053943 (DOI)
Funder
Swedish Research Council, 2009-5344
Available from: 2013-02-04 Created: 2013-01-31 Last updated: 2017-12-06Bibliographically approved

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