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Readability Metrics for Network Visualization Cody Dunne and Ben Shneiderman Human-Computer Interaction Lab & Department of Computer Science University.

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Presentation on theme: "Readability Metrics for Network Visualization Cody Dunne and Ben Shneiderman Human-Computer Interaction Lab & Department of Computer Science University."— Presentation transcript:

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2 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, 2009College Park, MD

3 Citations between papers in the ACL Anthology Network

4 NetViz Nirvana 1.Every node is visible 2.Every node’s degree is countable 3.Every edge can be followed from source to destination 4.Clusters and outliers are identifiable

5 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

6 Specific RMs Node Occlusion – Proportional to number of distinguishable items – 1: Each node is uniquely distinguishable – 0: All nodes overlap in connected mass CB D A

7 Specific RMs (cont) Edge Crossing – Number of crossings scaled by approximate upper bound CB D A

8 Specific RMs (cont) Edge Tunnels Number of tunnels scaled by approximate upper bound Local Edge Tunnels Triggered Edge Tunnels CB D A

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

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

11 Demo

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

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

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

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

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

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

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

19 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. Shneiderman First Steps to NetViz Nirvana: Evaluating Social Network Analysis with NodeXL Submitted, 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. Gleave Analyzing (Social Media) Networks with NodeXL C&T '09: Proc. Fourth international conference on Communities and Technologies, Springer, 2009

20 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

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

22 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

23 Layout: Force-Directed Layout

24 Contrasts in meaning between thesaurus categories

25 Interactions between graph-summarized groups proteins within the human body

26 Collaboration between cancer research organizations


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