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Interactive Navigation of Large Graphs and Networks Tamara Munzner Stanford University Graphics Group, CS Dept

Interactive Navigation of Large Graphs and Networks Tamara Munzner Stanford University Graphics Group, CS Dept

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Page 1: Interactive Navigation of Large Graphs and Networks Tamara Munzner Stanford University Graphics Group, CS Dept

Interactive Navigation of Large Graphs and Networks

Tamara MunznerStanford University Graphics Group, CS Dept

Page 2: Interactive Navigation of Large Graphs and Networks Tamara Munzner Stanford University Graphics Group, CS Dept

Overview

Cognitive psychology wish listInfoVis problemsGraph drawing examples

graph == network node-link as in graph theory not bar charts

Page 3: Interactive Navigation of Large Graphs and Networks Tamara Munzner Stanford University Graphics Group, CS Dept

Interaction

Fluid interaction is hallmark of modern CG

Can create non-real environments Example: navigation

RW: rigid motion across fixed terrain CG: distortion, warping of

structure/spacewhen (if ever) is this useful?

Page 4: Interactive Navigation of Large Graphs and Networks Tamara Munzner Stanford University Graphics Group, CS Dept

Wanted: Prescriptive Advice

Have for static 2D Gestalt, Bertin, Mackinlay, etc

My wish list: dynamic, spatial

when are distortion (focus+context) systems useful?

Page 5: Interactive Navigation of Large Graphs and Networks Tamara Munzner Stanford University Graphics Group, CS Dept

Process

[Distill domain knowledge into explicit problem statement]

Find visual technique(s) to help solve problem with preattentive processing

Feedback: is system solving problem? Are they using it? Does it help?

Page 6: Interactive Navigation of Large Graphs and Networks Tamara Munzner Stanford University Graphics Group, CS Dept

CP wish list, cont.

Analyze what’s good when: Tease apart complex visual metaphors

into constituent low-level components Conceptual framework, user studies

Backmapping: once know what it’s good for, what other domains can be abstracted into this problem?

Page 7: Interactive Navigation of Large Graphs and Networks Tamara Munzner Stanford University Graphics Group, CS Dept

Problems

Scalability real-world datasets consistently outstrip tools

Efficacy lack of rigor in evaluating viz systems

Novelty creating new visual metaphors is difficult

Adoption end user buy-in

Page 8: Interactive Navigation of Large Graphs and Networks Tamara Munzner Stanford University Graphics Group, CS Dept

Graph drawing: traditional

Static++ interactivity not inherent pan/scroll/zoom substitute for really big paper

Does not scale small (dozens, hundreds, maybe thousands) aggregation/abstraction of large datasets

great for expository, poor for exploratory

spring-force, circular, hierarchical, etc dot, daVinci, Tom Sawyer, etc

Page 9: Interactive Navigation of Large Graphs and Networks Tamara Munzner Stanford University Graphics Group, CS Dept

Three Interactive Systems

Geographic: Planet MulticastHyperbolic: H3/H3ViewerImportance gradient: Constellation

Page 10: Interactive Navigation of Large Graphs and Networks Tamara Munzner Stanford University Graphics Group, CS Dept

Geographic: Planet Multicast

Target users: Mbone maintainersTechnique: arcs on globe [Eick95]Task: identify long-haul

misconfigurations

Page 11: Interactive Navigation of Large Graphs and Networks Tamara Munzner Stanford University Graphics Group, CS Dept

Planet Multicast excerpt

Interaction techniques: click on arc for tunnel info rotate globe around center

selective occlusion

rotate around point on surfacehorizon view disambiguates

Page 12: Interactive Navigation of Large Graphs and Networks Tamara Munzner Stanford University Graphics Group, CS Dept

PM analysis

Visual popout: long-distance links cluttered tunnel topology

Literal & natural: no explanations needed

Filtering: intercity not intracity 4000 -> 700 hemisphere occlusion

Page 13: Interactive Navigation of Large Graphs and Networks Tamara Munzner Stanford University Graphics Group, CS Dept

PM efficacy

geog distance only approximates bottleneck sources ideal data uncollectable few false positives, many false negatives

IP address -> lat/lon mapping infeasibleAdoption:

maintainer coauthor during developmentDoes it help?

Anecdotal

Page 14: Interactive Navigation of Large Graphs and Networks Tamara Munzner Stanford University Graphics Group, CS Dept

Hyperbolic: H3Viewer

Target users: webmasters, gawkersTechnique:

uses 3D hyperbolic space for recursive hemispherical layout and navigation

spanning tree backbone with nontree links drawn on demand

guaranteed frame rate drawing algorithmTask: show context of surfing choices

Page 15: Interactive Navigation of Large Graphs and Networks Tamara Munzner Stanford University Graphics Group, CS Dept

H3Viewer video

Page 16: Interactive Navigation of Large Graphs and Networks Tamara Munzner Stanford University Graphics Group, CS Dept

H3 analysis

Visual popout: subtree clusters

Filtering: spanning tree in default case

Scalability: >100,000 nodes

Page 17: Interactive Navigation of Large Graphs and Networks Tamara Munzner Stanford University Graphics Group, CS Dept

H3 Efficacy

Adoption: Ships with SGI Irix 6.2-6.5

minor user testinguser feedback in the wild extremely minimal

PD code, other developersnetworking, genetic algorithms

Does it help? Real user study prelim results promising

Page 18: Interactive Navigation of Large Graphs and Networks Tamara Munzner Stanford University Graphics Group, CS Dept

Importance Gradient: Constellation

Target users: two linguists at MSRTechniques:

custom spatial layout, horiz gradient careful use of HSV to minimize clutter

impactTask: debug semantic network

creation find implausible computed paths

Page 19: Interactive Navigation of Large Graphs and Networks Tamara Munzner Stanford University Graphics Group, CS Dept

Constellation video

Page 20: Interactive Navigation of Large Graphs and Networks Tamara Munzner Stanford University Graphics Group, CS Dept

Constellation analysis

Visual popout: hotspots, highlighted structures

Filtering: none design principle of avoiding hidden state

Adoption: TBD still under development pros and cons of tiny user community

Page 21: Interactive Navigation of Large Graphs and Networks Tamara Munzner Stanford University Graphics Group, CS Dept

Conclusion

Key problem in the field: Evaluating efficacy Scalability

Exploration of the design space three quite different GD systems

Page 22: Interactive Navigation of Large Graphs and Networks Tamara Munzner Stanford University Graphics Group, CS Dept

Acknowledgements

Planet Multicast: Eric Hoffman, Kim Claffy, Bill Fenner

Site Manager: Greg Ferguson, Alan Braverman, Ken

KershnerConstellation:

Francois Guimbretiere, George RobertsonAdvisor: Pat Hanrahan

Page 23: Interactive Navigation of Large Graphs and Networks Tamara Munzner Stanford University Graphics Group, CS Dept

More info

http://graphics.stanford.edu/~munzner papers talks software