Browsing by Author "Vehlow, Corinna"

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Open Access
Visualization techniques for group structures in graphs
(2015) Vehlow, Corinna; Weiskopf, Daniel (Prof. Dr.)
Graph visualization plays a key role analyzing relations between objects. With increasing size of the graph, it becomes difficult to understand global and local structures of the graph. Grouping objects of the graph based on their attributes or relations helps reveal global structures. Visualizing these group structures together with the graph topology can highlight central objects and reveal outliers. The ability of a visualization to help detecting these features becomes more difficult for groups that overlap or change over time. In many applications, groups cannot be interpreted as disjoint sets of objects. In fact, objects are often involved in several groups, sometimes even to different extent. With the existing types of overlapping groups, further analysis tasks arise that need to be considered for the visualization. In addition, real-world scenarios are not static but change over time and so do relations among objects. With the graph topology changing over time, the group structure changes as well. The challenge for visualizations of dynamic groups in dynamic graphs is to facilitate the analysis of group-related features not only for individual points in time but over time, showing group evolution events. This thesis presents visualization techniques for group structures in graphs that address these challenges: overlap and time dependency. As a basis, a survey of the state of the art in visualizing group structures in graphs is presented. The first part of this thesis is dedicated to the visualization of overlapping groups in static graphs, where different types of overlaps are considered. With each technique, the complexity of the groups increases. First, a visual analytics system for crisp overlapping groups in multivariate graphs is presented. This system integrates interactive filtering of large and dense networks with groupbased layouts of the resulting subnetworks and a technique to compare those subnetworks. Second, a technique that visualizes fuzzy overlapping groups in a graph based on layout strategies and further visual mappings is presented. This technique facilitates the investigation of fuzzy group memberships at different levels of detail based on a hierarchical aggregation model. In contrast to these techniques, the third visualization technique shows groups based on multivariate edge attributes rather than vertex attributes or the topology of the graph. In particular, edge-edge relations are visualized as curves that are directly integrated into the node-link diagram representing the object-relation structure. The second part is dedicated to visualization techniques for dynamic groups in dynamic graphs. Again, the complexity of the group structure rises from the first technique addressing flat groups to the second technique addressing more complex hierarchical groups. Within both techniques, the evolution of groups is encoded using a flow metaphor. The first technique visualizes the partially aggregated graphs by node-link diagrams, whereas the second technique is based on an extended adjacency matrix representation that encodes the hierarchical structure of vertices as well as changes in the graph topology. All presented techniques visualize the group structure integrated with the graph topology in a single image. Finally, the use of all techniques is demonstrated for real data sets from biology, one of the main application domains of group structures in graphs.