Static Spatial Graph Features
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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
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Overview
• Motivation• Static Spatial Graph Features• User Studies• Results• Summary• Conclusion
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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
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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?
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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?
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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?
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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
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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
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Virtual Landmarks
• Idea: Add visual landmarks as static reference points that can be used for orientation
• Landmarks– Discrete objects– Evenly distributed invisual space
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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
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Phase I: Learning• N blinking nodes shown in sequence, Participants visit and learn their positions.
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Phase I: Learning (cont’d)
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Phase II: Revisitation• Participants revisit the nodes whose location they had learned, in the same order
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Phase II: Revisitation
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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
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Study 1: Results
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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
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Study 2: Results
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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
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Study 3: Results
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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
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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
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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
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Thank You!
Contact Information:Sohaib GhaniSchool of Electrical & Computer EngineeringPurdue UniversityE-mail: [email protected]
http://engineering.purdue.edu/pivot/