Publikation: Multiscale Visual Analysis of Dynamic Networks
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Networks are a universal language for modeling the underlying structure of real-world systems, such as computer networks, social networks, or financial networks. Many of these modeled real-world systems are dynamic, meaning the relationships between the entities change over time. A central goal in dynamic (temporal) network analysis is to discover similar network structures and retrace structural changes over time. However, visually analyzing dynamic networks remains challenging due to large-scale data often evolving over long periods. This thesis presents studies for the multiscale visual analysis of dynamic networks. The presented studies introduce multiscale dynamic network visualizations for identifying, comparing, tracing, and interpreting similar network structures over time. The proposed visualizations combine automated analysis methods with interactive visualizations to reveal evolving network structures across multiple abstraction scales (multiscale analysis). The presented multiscale visualizations scale to large-scale dynamic networks and enable analysts to relate high-level overviews with low-level details to reveal structural changes and similar network structures over time. The presented studies are showcased by prototype implementations using real-world datasets and are validated with domain experts, quantitative evaluations, and use cases. Moreover, the thesis systematically discusses the benefits and limitations of the presented studies and outlines future research perspectives.
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CAKMAK, Eren, 2022. Multiscale Visual Analysis of Dynamic Networks [Dissertation]. Konstanz: University of KonstanzBibTex
@phdthesis{Cakmak2022Multi-59742, year={2022}, title={Multiscale Visual Analysis of Dynamic Networks}, author={Cakmak, Eren}, address={Konstanz}, school={Universität Konstanz} }
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