Landesberger von Antburg, Tatiana ; Görner, Melanie ; Schreck, Tobias (2009)
Visual Analysis of Graphs with Multiple Connected Components.
IEEE Symposium on Visual Analytics Science and Technology 2009. Proceedings.
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
In this paper, we present a system for the interactive visualization and exploration of graphs with many weakly connected components. The visualization of large graphs has recently received much research attention. However, specific systems for visual analysis of graph data sets consisting of many such components are rare. In our approach, we rely on graph clustering using an extensive set of topology descriptors. Specifically, we use the Self-Organizing- Map algorithm in conjunction with a user-adaptable combination of graph features for clustering of graphs. It offers insight into the overall structure of the data set. The clustering output is presented in a grid containing clusters of the connected components of the input graph. Interactive feature selection and task-tailored data views allow the exploration of the whole graph space. The system provides also tools for assessment and display of cluster quality. We demonstrate the usefulness of our system by application to a shareholder structure analysis problem based on a large real-world data set. While so far our approach is applied to weighted directed graphs only, it can be used for various graph types.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2009 |
| Autor(en): | Landesberger von Antburg, Tatiana ; Görner, Melanie ; Schreck, Tobias |
| Art des Eintrags: | Bibliographie |
| Titel: | Visual Analysis of Graphs with Multiple Connected Components |
| Sprache: | Englisch |
| Publikationsjahr: | 2009 |
| Verlag: | IEEE Press, New York |
| Veranstaltungstitel: | IEEE Symposium on Visual Analytics Science and Technology 2009. Proceedings |
| Kurzbeschreibung (Abstract): | In this paper, we present a system for the interactive visualization and exploration of graphs with many weakly connected components. The visualization of large graphs has recently received much research attention. However, specific systems for visual analysis of graph data sets consisting of many such components are rare. In our approach, we rely on graph clustering using an extensive set of topology descriptors. Specifically, we use the Self-Organizing- Map algorithm in conjunction with a user-adaptable combination of graph features for clustering of graphs. It offers insight into the overall structure of the data set. The clustering output is presented in a grid containing clusters of the connected components of the input graph. Interactive feature selection and task-tailored data views allow the exploration of the whole graph space. The system provides also tools for assessment and display of cluster quality. We demonstrate the usefulness of our system by application to a shareholder structure analysis problem based on a large real-world data set. While so far our approach is applied to weighted directed graphs only, it can be used for various graph types. |
| Freie Schlagworte: | Forschungsgruppe Visual Search and Analysis (VISA), Clustering, Graphical user interfaces (GUI), Image generation, Graphs, Self-organizing maps (SOM) |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 12 Nov 2018 11:16 |
| Letzte Änderung: | 22 Jul 2021 18:31 |
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