Interactive Visual Exploration Interface for Large Bipartite Networks

Information

• Publication Type: Master Thesis
• Workgroup(s)/Project(s):
  • Visual Information Foraging on the Desktop
• Date: May 2018
• Date (Start): July 2017
• Date (End): 30. May 2018
• Open Access: yes
• First Supervisor:
  • Eduard Gröller
  • Manuela Waldner

Abstract

In this thesis we introduce BiCFlows, a novel interactive visualization approach to explore large bipartite graphs. We were motivated by the Media Transparency Database, a public database established by the Austrian government to provide information about governmental advertising and subsidies expenses, which holds the characteristics of a large, weighted bipartite graph. Current approaches that deal with the visualization of the Media Transparency Database are limited by the fact that they do not offer a sufficient overview of the whole dataset. Other existing approaches that are not particularly designed for the Media Transparency Database, but deal with the visualization of bipartite graphs are in addition limited by their lack of scalability for large datasets. Aggregation is an often used concept in reducing the amount of data by grouping together similar data objects. This only works if the appropriate object properties are present in the data to use them for the aggregation. If this additional information is missing, like in the Media Transparency Database, other aggregation techniques have to be used. Since we are dealing with bipartite graphs in our approach, we use the concept of biclustering to establish a hierarchical structure within the data that can be interactively explored by the user. We showed that BiCFlows cannot only be used for the Media Transparency Database, but also for other datasets that share the characteristics of a weighted bipartite graph. Furthermore, we conducted an insight-based user study to compare BiCFlows with existing concepts and discussed advantages and drawbacks. We showed that BiCFlows supported users in their exploration process and let them gain more insight than with existing approaches.

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BibTeX

@mastersthesis{steinboeck-2017-vbn,
  title =      "Interactive Visual Exploration Interface for Large Bipartite
               Networks",
  author =     "Daniel Steinb\"{o}ck",
  year =       "2018",
  abstract =   "In this thesis we introduce BiCFlows, a novel interactive
               visualization approach to explore large bipartite graphs. We
               were motivated by the Media Transparency Database, a public
               database established by the Austrian government to provide
               information about governmental advertising and subsidies
               expenses, which holds the characteristics of a large,
               weighted bipartite graph. Current approaches that deal with
               the visualization of the Media Transparency Database are
               limited by the fact that they do not offer a sufficient
               overview of the whole dataset. Other existing approaches
               that are not particularly designed for the Media
               Transparency Database, but deal with the visualization of
               bipartite graphs are in addition limited by their lack of
               scalability for large datasets. Aggregation is an often used
               concept in reducing the amount of data by grouping together
               similar data objects. This only works if the appropriate
               object properties are present in the data to use them for
               the aggregation. If this additional information is missing,
               like in the Media Transparency Database, other aggregation
               techniques have to be used. Since we are dealing with
               bipartite graphs in our approach, we use the concept of
               biclustering to establish a hierarchical structure within
               the data that can be interactively explored by the user. We
               showed that BiCFlows cannot only be used for the Media
               Transparency Database, but also for other datasets that
               share the characteristics of a weighted bipartite graph.
               Furthermore, we conducted an insight-based user study to
               compare BiCFlows with existing concepts and discussed
               advantages and drawbacks. We showed that BiCFlows supported
               users in their exploration process and let them gain more
               insight than with existing approaches.",
  month =      may,
  address =    "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria",
  school =     "Institute of Computer Graphics and Algorithms, Vienna
               University of Technology ",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2018/steinboeck-2017-vbn/",
}