TU Darmstadt / ULB / TUbiblio

Visual Analysis of Graphs with Multiple Connected Components

Landesberger, Tatiana von and Görner, Melanie and Schreck, Tobias (2009):
Visual Analysis of Graphs with Multiple Connected Components.
IEEE Press, New York, In: IEEE Symposium on Visual Analytics Science and Technology 2009. Proceedings, [Conference or Workshop Item]

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.

Item Type: Conference or Workshop Item
Erschienen: 2009
Creators: Landesberger, Tatiana von and Görner, Melanie and Schreck, Tobias
Title: Visual Analysis of Graphs with Multiple Connected Components
Language: English
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.

Publisher: IEEE Press, New York
Uncontrolled Keywords: Forschungsgruppe Visual Search and Analysis (VISA), Clustering, Graphical user interfaces (GUI), Image generation, Graphs, Self-organizing maps (SOM)
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Event Title: IEEE Symposium on Visual Analytics Science and Technology 2009. Proceedings
Date Deposited: 12 Nov 2018 11:16
Export:

Optionen (nur für Redakteure)

View Item View Item