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Readability Metrics for Network Visualization

Readability Metrics for Network Visualization. Cody Dunne and Ben Shneiderman Human-Computer Interaction Lab & Department of Computer Science University of Maryland Contact: cdunne@cs.umd.edu 26 th Annual Human-Computer Interaction Lab Symposium May 28-29, 2009 College Park, MD.

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Readability Metrics for Network Visualization

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  1. Readability Metrics for Network Visualization Cody Dunne and Ben Shneiderman Human-Computer Interaction Lab & Department of Computer Science University of Maryland Contact: cdunne@cs.umd.edu 26th Annual Human-Computer Interaction Lab Symposium May 28-29, 2009 College Park, MD

  2. Citations between papers in the ACL Anthology Network

  3. NetViz Nirvana • Every node is visible • Every node’s degree is countable • Every edge can be followed from source to destination • Clusters and outliers are identifiable

  4. Readability Metrics • How understandable is the network drawing? • Continuous scale [0,1] • Example: Journal may recommend • 0% node occlusion • <2% edge tunneling • <5% edge crossing • Also called aesthetic metrics • Global metrics are not sufficient to guide users • Node and edge readability metrics

  5. Specific RMs • Node Occlusion • Proportional to number of distinguishable items • 1: Each node is uniquely distinguishable • 0: All nodes overlap in connected mass C B A D

  6. Specific RMs (cont) • Edge Crossing • Number of crossings scaled by approximate upper bound A C B D

  7. Specific RMs (cont) • Edge Tunnels • Number of tunnels scaled by approximate upper bound • Local Edge Tunnels • Triggered Edge Tunnels C A B D

  8. SocialAction • Social network analysis tool • Statistical measures • Attribute ranking • Multiple coordinated views • Papers: • A. Perer and B. ShneidermanBalancing Systematic and Flexible Exploration of Social NetworksIEEE Transactions on Visualization and Computer Graphics, 2006, 12, 693-700 • A. Perer and B. ShneidermanIntegrating statistics and visualization: case studies of gaining clarity during exploratory data analysisCHI '08: Proceeding of the 26th annual SIGCHI Conference on Human Factors in Computing Systems, ACM, 2008, 265-274 • A. Perer and B. ShneidermanSystematic yet flexible discovery: guiding domain experts through exploratory data analysisIUI '08: Proc. 13th International Conference on Intelligent User Interfaces, ACM, 2008, 109-118

  9. Contributions • Global readability metrics • Node and edge readability metrics • Real-time RM feedback as nodes are moved • Integrated into attribute ranking system

  10. Demo

  11. Rank by: Node Occlusion Node occlusion: 14 Edge tunnels: 70 Edge crossings: 180 Spring coeff:

  12. Rank by: Node Occlusion Node occl: 4(-10) Edge tunnel: 26(-44) Edge cross: 159(-21) Spring coeff:

  13. Rank by: Node Occlusion Node occl: 0(-4) Edge tunnel: 14(-12) Edge cross: 157(-2) Spring coeff:

  14. Rank by: Local Edge Tunnel Node occl: 0(-0) Edge tunnel: 14(-0) Edge cross: 157(-0) Spring coeff:

  15. Rank by: Local Edge Tunnel Node occl: 0 (-0) Edge tunnel: 0(-14) Edge cross: 155(-2) Spring coeff:

  16. Rank by: Edge Crossing Node occl: 0(-0) Edge tunnel: 0(-0) Edge cross: 155(-0) Spr.coeff:

  17. Rank by: Edge Crossing Node occl: 0(-0) Edge tunnel: 0(-0) Edge cross: 85(-70) Spr.coeff:

  18. Future Work • Snap-to-Grid tool pulls node to local maxima • Feedback for layout algorithms • Evaluation • NetViz Nirvana useful for teaching network analysis • E. M. Bonsignore, C. Dunne, D. Rotman, M. Smith, T. Capone, D. L. Hansen and B. ShneidermanFirst Steps to NetViz Nirvana: Evaluating Social Network Analysis with NodeXLSubmitted, 2009 • Integration into NodeXL to test RM effectiveness • www.codeplex.com/nodexl • M. Smith, B. Shneiderman, N. Milic-Frayling, E. M. Rodrigues, V. Barash, C. Dunne, T. Capone, A. Perer and E. GleaveAnalyzing (Social Media) Networks with NodeXLC&T '09: Proc. Fourth international conference on Communities and Technologies, Springer, 2009

  19. Conclusion • Global RMs to judge readability of network drawings • Node and Edge RMs for interactive identification of problem areas • Network analysts and designers of tools should take drawing readability into account

  20. Paper C. Dunne and B. ShneidermanImproving Graph Drawing Readability by Incorporating Readability Metrics: A Software Tool for Network AnalystsHCIL Tech Report HCIL-2009-13, Submitted, 2009 Contact cdunne@cs.umd.edu

  21. Additional RMs • Angular Resolution • Edge Crossing Angle • Node Size • Node Label Distinctiveness • Text Legibility • Node Color & Shape Variance • Orthogonality • Spatial Layout & Grouping • Symmetry • Edge Bends • Path Continuity • Geometric-path Tendency • Path Branches • Edge Length

  22. Layout: Force-Directed Layout

  23. Contrasts in meaning between thesaurus categories

  24. Interactions between graph-summarized groups proteins within the human body

  25. Collaboration between cancer research organizations

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