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GrouseFlocks: steerable exploration of graph hierarchy space
Daniel Archambault1, Tamara Munzner, David Auber
1Department of Computer Science, University of British Columbia, Vancouver BC, Canada. archam@cs.ubc.ca
Summary
GrouseFlocks enables interactive exploration of graph hierarchy space using domain-specific attributes. This system allows users to discover multiple unique graph views beyond fixed hierarchies, facilitating deeper data insights.
Area of Science:
- Computer Science
- Data Visualization
- Graph Theory
Background:
- Previous systems explore graphs via hierarchies, often derived from clustering or topology.
- Graphs possess domain-specific node/edge attributes that can generate diverse hierarchies.
- Existing methods limit exploration to single, pre-defined hierarchies.
Purpose of the Study:
- Introduce GrouseFlocks for interactive exploration of graph hierarchy space.
- Enable users to discover and investigate multiple unique graph views.
- Facilitate hierarchy generation based on graph attributes and user selections.
Main Methods:
- Develop GrouseFlocks, a system for graph hierarchy exploration.
- Implement user-driven operations for creating and modifying hierarchies.
- Support selections based on manual input or attribute data patterns.
- Ensure rapid feedback for interactive exploration.
Main Results:
- GrouseFlocks allows visualization of multiple hierarchies for a single graph.
- Users can interactively create and refine hierarchies using attribute-based selections.
- The system provides near real-time feedback for dynamic exploration.
- Facilitates investigation of graph hierarchy space beyond fixed structures.
Conclusions:
- GrouseFlocks enhances graph exploration by leveraging attribute data for diverse hierarchy generation.
- Interactive manipulation of hierarchies based on attributes reveals unique data perspectives.
- The system supports efficient and insightful exploration of complex graph structures.
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