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Readability Metrics for Network Visualization

At the 26th Annual Human-Computer Interaction Lab Symposium, Cody Dunne and Ben Shneiderman presented a framework to improve the readability of network visualizations. The proposed readability metrics assess the clarity of network drawings through specific measures such as node occlusion, edge crossings, and edge tunnels. These metrics guide users in identifying clusters and outliers, enhancing understanding during social network analysis. This work emphasizes the integration of real-time feedback and the importance of aesthetic considerations in network design, paving the way for more effective exploration of complex data.

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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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