Visualizing Change over Time: the Pipes Metaphor
Jim Blythe
USC Information Sciences Institute
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Talk outline
 Why pipes??
 Visualizing, interacting with and animating flow.
 Scaling up: flow in citation and hats data with 1m nodes+links
 The emerging semantics/provenance of a visualization
 Wrap-up
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Motion-based visualization for relational spatio-temporal (RST) data
 Motion is a natural tool for visualizing RST data
 Draws human attention to change in complex data
 Can reflect temporal aspects directly
 Natural way to provide feedback on analyst ‘what-if’ tests
 Interaction essential to avoid false impressions due to dimension reduction.
 But we don’t yet have a theory or conventions for using motion in network visualizations
 Users interpret visual cues in rigid ways, both from cognitive aspects and via a rich set of pre-established conventions
 Fewer experimental results for motion (but see work by
Ware and others)
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Our solution: visualizations based on physical analogies
 By appealing to well-known physical analogies, provide interpretable images and ways to interact with the data.
 Users will bring expectations about how to interpret observed motion and change
 Examples studied: fluid flow through elastic pipes and wave motion through a graph
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Why pipes??
Fluid flow in pipes is a simple and common model , not fully exploited for interacting with networks.
 Everyone is familiar with it: intuitive and powerful
 Provides natural interactions with a network
 Puts focus on links, rather than well-studied nodes
 A general method – for oil/gas, money, information or ideas, actual or max flow, ..
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Impact for analysts
 Facilitate what-if testing of actions on networks
 Bring computed network properties to life through interaction with fast feedback
 Facilitate understanding of observed and possible future dynamics of underlying system
 Draw attention to widely distributed effects of action as network changes
 Enable rapid identification of relevant data, regardless of how they are distributed relationally, spatially, or temporally
 General techniques that can be applied to, e.g., disease outbreaks, terrorist networks, flow of money
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Talk outline
 Why pipes??
 Visualizing, interacting with and animating flow.
 Scaling up: flow in citation and hats data with 1m nodes+links
 The emerging semantics/provenance of a visualization
 Wrap-up
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Example: trade in widgets
Links show capacities.
E.g. “What is the greatest number of widgets F can receive from A and B?”
Standard max flow problems are easy to solve, but are NP-hard if e.g.
probabilities are involved.
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Simple use of pipes
2d ‘pipe’ shows capacity and flow.
Red shows flow.
Black shows unused capacity.
If I must remove one link, which has the least effect? The greatest?
If I can increase one link in capacity, which should I choose?
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Min cut
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Scaling up: satellite data on oil and gas pipelines
 ~3000 nodes, 3000 links, each with up to 80 line segments
 Flow updated ~50 times per second (on 1.6 GHz centrino, 512M RAM)
 Helps users pinpoint areas most vulnerable to attack, or high-payoff areas to improve capacity in real time.
 See our demo.
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Generic action API for 3 rd -party code
Users can
 Add and modify nodes, links and capacities
 Set node properties, e.g. source, sink, …
 Temporarily block nodes and links
3 rd -party systems can
 Declare node or link properties of interest
 Register for user modifications
 Provide updated data on links and nodes
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Talk outline
 Why pipes??
 Visualizing, interacting with and animating flow.
 Scaling up: flow in citation and hats data with 1m nodes+links
 The emerging semantics/provenance of a visualization
 Wrap-up
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Scaling up: Cora publication dataset
 Dataset of citations with 220k nodes and 700k links
 32k papers have year, title, journal/conference
 Links are ‘person a is an author of paper b’, or ‘paper a cites paper b’
 Complete graph loads in seconds in KrackPlot.
 Define filters and graph manipulations for visualization on the fly.
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Timeline view of single author
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Timeline view of single author
1992: Author moves from CMU to ISI
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Flow applications in publication data
 Identify keywords or key phrases in part of the text
 Have access to title in CORA
 Visualize flow from paper to citing paper when rare phrases are repeated
 Uncover influence between individuals and communities, or track the emergence of a sub-field
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Provenance and semantics of a visualization
A number of manipulations must be choreographed to create the most useful visualizations
 Some are at data/filtering level – what entities are shown as nodes and as links
 Some are at the visual level – assigning visual cues to properties of the data
Together they form the provenance, or semantics, of the visualization
When another viewer sees the visualization, they will need to know the semantics to understand it
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Example from timeline display
Goal:
Show co-authors and cocitations on a timeline for “name”
Actions required to create timeline display:
Search $name -> $node display only current selection show neighborhood size 2 around $node unselect $node add child links define nbrhd "focal + children + parents|entity=person“ around $node map year to time in anneal layout lay out by annealing show classification as color and shape
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Maintaining provenance to create shareable visualizations – and reusing them
The visual system captures the user’s actions and generalizes them by analyzing dependencies
Allows replay on other points of interest
- e.g. a new reusable process to visualize author timelines
Can be encapsulated with the visualization to provide provenance
- another viewer can inspect the filtering and visual cues used
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Hats output datasets
Tested loading of output with 20k hats and ~ 1.3million links
Interesting sequences of meetings can be viewed as a flow of capabilities
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Impact for analysts
 Facilitate what-if testing of actions on networks
 Bring computed network properties to life through interaction with fast feedback
 Facilitate understanding of observed and possible future dynamics of underlying system
 Draw attention to widely distributed effects of action as network changes
 Enable rapid identification of relevant data, regardless of how they are distributed relationally, spatially, or temporally
 General techniques that can be applied to, e.g., disease outbreaks, terrorist networks, flow of money
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Highlighting change
Motion:
 Node/link motion to reflect structural change
 Highlighting links: moving bulges, links that fade, radiate, wiggle or flash
Static:
 Show the difference with overlays using color, background shading, …
Build on our work on motion in social network viz
[McGrath & Blythe 04] and work in general viz
[Bartram et al 01].
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Status and future work
 Completed tasks on initial animation and interaction
 Defining user tasks to test utility – uncover vulnerabilities or leverage points in flow
 Or discover influential actors in publication space
 Next tasks: use animations to show consequences of interaction or change in structure over time.
 Push on interesting ideas on emerging semantics of visualizations in collaborative environments.
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Back-up slides
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