Interactive Visual Exploration of Large Bipartite Networks (DAEV)

In this thesis, we present BiCFlows, a novel approach to interactively 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.