static spatial graph features
DESCRIPTION
Presentation from GI 2011 for our paper on "Improving Graph Revisitation Using Static Spatial Features".TRANSCRIPT
Improving Revisitation in
Graphs through Static Spatial
Features
Sohaib GhaniPurdue University
West Lafayette, IN, USA
Graphics Interface 2011May 25-27, 2011 ▪ St. John’s Newfoundland, Canada
Niklas Elmqvist
Purdue UniversityWest Lafayette, IN, USA
Presented by
Pourang IraniUniversity of Manitoba
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Basic Idea
Overview
• Motivation• Static Spatial Graph Features• User Studies• Results• Summary• Conclusion
Memorability & Revisitation
MemorabilityThe memorability of a visual space is a measure of a user’s ability to remember information about the space
RevisitationRevisitation is the task of remembering where objects in the visual space are located and how they can be reached
Motivation• Graphs prevalent in many information tasks– Social network analysis (Facebook, LinkedIn, Myspace)– Road networks and migration patterns– Network topology design
• Graphs often visualized as node-link diagrams• Node-link diagrams have few spatial features– Low memorability– Difficult to remember for revisitation
• Research questions– How to improve graph memorability?– How to improve graph revisitation performance?
Example: Social Network Analysis
• Interviewed two social scientists who use graphs for Social Network Analysis (SNA)
• Often experience trouble in orienting themselves in a social network when returning to previously studied network
• At least 50% of all navigation in SNA in previously visited parts of a graph
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• People remember locations in visual spaces using spatial features and landmarks
• Geographical maps have many spatial features and are easy to remember
• Evaluate whether static spatial features to node-link diagrams help in graph revisitation– Inspired by geographic maps
Idea: Spatial Features in NL Diagrams?
Design Space:Static Spatial Graph Features
• Three different techniques of adding static spatial features to graphs– Substrate Encoding (SE)– Node Encoding (NE)– Virtual Landmarks (LM)
• But which technique is optimal?
Substrate Encoding• Idea: Add visual features to substrate (canvas)• Partitioning of the space into regions– Space-driven: split into regions of equal size– Detail-driven: split into regions with equal numbers of items
• Encoding identity into each region– Color– Textures
Figure 1 Figure 2
Node Encoding
• Idea: Encode spatial position into the nodes (and potentially the edges) of a graph
• Available graphical variables:– Node Size– Node Shape– Node Color
Virtual Landmarks
• Idea: Add visual landmarks as static reference points that can be used for orientation
• Landmarks– Discrete objects– Evenly distributed invisual space
User Studies
• Experimental Platform– Node-link graph viewer in Java– Overview and detail windows
• Participants: 16 paid participants per study
• Task: Graph revisitation–Phase I: Learning–Phase II: Revisitation
Phase I: Learning• N blinking nodes shown in sequence, Participants visit and learn their positions.
Phase I: Learning (cont’d)
Phase II: Revisitation• Participants revisit the nodes whose location they had learned, in the same order
Phase II: Revisitation
Study 1: Substrate Encoding
• Study Design:– Partitioning: Grid and Voronoi Diagram.– Identity Encoding: Color and Texture– Layout: Uniform and Clustered
• Hypotheses:– Voronoi diagram will be faster and more accurate than grid for spatial partitioning
– Texture will be more accurate than color for identity encoding
Study 1: Results
Study 2: Node Encoding
• Study Design:– 3 Node Encoding techniques: Size, Color and Size+Color
• Hypothesis:– Size and color combined will be the best node encoding technique in terms of both time and accuracy
Study 2: Results
Study 3: Combinations
• Best techniques from Study 1 (Grid with Color) and Study 2 (Size+Color) as well as virtual landmarks
• Study Design:– Eight different techniques: SE, NE, LM,SE+NE, SE+LM, NE+LM, SE+NE+LM, and simple graph (SG)
• Hypotheses:– Techniques utilizing substrate encoding will be faster and more accurate than node encoding and landmarks
– The combination of all three spatial graph feature techniques will be fastest and most accurate
Study 3: Results
Study 3: Results (cont’d)
• Techniques with substrate encoding significantly faster and not less accurate.
• SE+NE+LM not significantly faster and more accurate than all other techniques
• Virtual landmarks promising strategy, performing second only to substrate encoding
Summary
• Substrate encoding (SE) is dominant strategy– Space-driven partitioning– Solid color encoding
• Virtual landmarks (LM) help significantly• Node encoding (NE) not as good other two• Combination of virtual landmarks (LM) and substrate encoding (SE) is optimal
Conclusion
• Explored design space of adding static spatial features to graphs
• Performed three user studies– Study 1: grid with color is optimal substrate encoding
– Study 2: node size and color is optimal node encoding
– Study 3: substrate encoding, landmarks, and their combination are optimal techniques
Thank You!
Contact Information:Sohaib GhaniSchool of Electrical & Computer EngineeringPurdue UniversityE-mail: [email protected]
http://engineering.purdue.edu/pivot/