Reordering Massive Sequence Views: Enabling temporal and structural analysis of dynamic networks

Networks are present in many fields such as finance, sociology, and transportation. Often these networks are dynamic: they have a structural as well as a temporal aspect. We present a technique that extends the Massive Sequence View (MSV) for the analysis of the temporal and structural aspects of dy...

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Vydané v:2013 IEEE Pacific Visualization Symposium (PacificVis) s. 33 - 40
Hlavní autori: van den Elzen, Stef, Holten, Danny, Blaas, Jorik, van Wijk, Jarke J.
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.02.2013
Predmet:
Communities
I.3.3 [Computing Methodologies]: Computer Graphics-Picture/Image Generation
Image color analysis
Simulated annealing
Visualization
ISSN:2165-8765
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Abstract Networks are present in many fields such as finance, sociology, and transportation. Often these networks are dynamic: they have a structural as well as a temporal aspect. We present a technique that extends the Massive Sequence View (MSV) for the analysis of the temporal and structural aspects of dynamic networks. Using features in the data as well as in the visualization based on the Gestalt principles closure, proximity, and similarity, we developed node reordering strategies for the MSV to make these features stand out. This enables users to find temporal properties such as trends, counter trends, periodicity, temporal shifts, and anomalies in the network as well as structural properties such as communities and stars. We show the effectiveness of the reordering methods on both synthetic and real-world transaction data sets.
AbstractList Networks are present in many fields such as finance, sociology, and transportation. Often these networks are dynamic: they have a structural as well as a temporal aspect. We present a technique that extends the Massive Sequence View (MSV) for the analysis of the temporal and structural aspects of dynamic networks. Using features in the data as well as in the visualization based on the Gestalt principles closure, proximity, and similarity, we developed node reordering strategies for the MSV to make these features stand out. This enables users to find temporal properties such as trends, counter trends, periodicity, temporal shifts, and anomalies in the network as well as structural properties such as communities and stars. We show the effectiveness of the reordering methods on both synthetic and real-world transaction data sets.
Author van Wijk, Jarke J.
van den Elzen, Stef
Holten, Danny
Blaas, Jorik
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  givenname: Jarke J.
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  fullname: van Wijk, Jarke J.
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  organization: Eindhoven Univ. of Technol., Eindhoven, Netherlands
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I.3.3 [Computing Methodologies]: Computer Graphics-Picture/Image Generation
Image color analysis
Simulated annealing
Visualization
Title Reordering Massive Sequence Views: Enabling temporal and structural analysis of dynamic networks
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