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Visualizing Change over Time:the Pipes Metaphor

Jim BlytheUSC 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 visualizationfor 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

2-d ‘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?

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Min cut

If I can increase one link in capacity, which should I choose?

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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 3rd-party code

Users can Add and modify nodes, links and capacities Set node properties, e.g. source, sink, … Temporarily block nodes and links

3rd-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 movesfrom 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 co-citations 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