Authors:
Lionel Martin
1
and
Géraldine Bous
2
Affiliations:
1
EPFL and RTI, Switzerland
;
2
RTI, France
Keyword(s):
Graph Visualization, Social Networks, Graph Clustering, Machine Learning, User Interaction.
Related
Ontology
Subjects/Areas/Topics:
Abstract Data Visualization
;
Computer Vision, Visualization and Computer Graphics
;
Graph Visualization
;
Interface and Interaction Techniques for Visualization
;
Visual Data Analysis and Knowledge Discovery
;
Visual Representation and Interaction
Abstract:
We present a tool for the interactive exploration and analysis of large clustered graphs. The tool empowers users to control the granularity of the graph, either by direct interaction (collapsing/expanding clusters) or via a slider that automatically computes a clustered graph of the desired size. Moreover, we explore the use of learning algorithms to capture graph exploration preferences based on a history of user interactions. The learned parameters are then used to modify the action of the slider in view of mimicking the natural interaction/exploration behavior of the user.