amy vanderbilt ~ vin taylor ~ martin taylor ~ mark nixon ~ jan terje bjorke

Visualisation Network-of-ExpertsMalvern, UK

NOV 4-6th2008

Amy Vanderbilt ~ Vin Taylor ~ Martin Taylor ~ Mark Nixon ~ Jan Terje Bjorke Sven Brueckner ~ Zack Jacobson ~ Jason Moore ~ Rusty Bobrow

Information-Theoretic Considerations of Graph/Network Topology

Amy K. C. S. Vanderbilt, Ph.D. TITLE

(USA) 571-723-5645 [email protected]

Information-Theoretic Considerations of Graph/Network Topology

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OutlineOutline

Random Thoughts

Cognitive Model Revision

Effect of Hypernodes

Quantification of Network Visualizations

How Does Information Theory Support Visualization? – Random Philosophical Thoughts


High Level Process

A First Step At Quantification

Effect Of Hypernodes

Towards Quantification Of Network Visualizations

Measuring The Information Content In An Image

Measuring The Information Content In A Visualization

User Interaction And Optimization Using Information Theory

Tuning The Sources



HOW DOES INFORMATION THEORY SUPPORT VISUALIZATION?

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Random Philosophical thoughts

Use the visualization to convey syntax and semantics and let the user key off of their experience / world view (pragmatics)

Syntax ~ Semantics ~ Pragmatics : these three combine to yield a coherent understanding leading to accurate analysis

The function of visual capacity is the summation of the history plus what data you are presented and the manner in which it was presented

The measure of the information conveyed is how much you have reduced uncertainty (information entropy) from the cognitive model

Outline

Random Thoughts






HOW DOES INFORMATION THEORY SUPPORT VISUALIZATION?

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Random Philosophical thoughts

Information capacity is the difference between what you know and what is displayed

Can we use information theory to build visualizations tailored to what the user knows? i.e. their pragmatic history?

We have no control over what they user may get out of the visualization that is not there or that is beyond what is there

WORLD = the network the user is trying to understand + embedding fields [i.e. the user’s accumulated context/pragmatics]

Perception is an active process consisting of interaction with the environment

Outline

Random Thoughts




Pragmatic Context UserSyntax & Semantics Data System

Happens in the

User’s Mind




5VISUALIZATION IS USER-CENTRIC

High Level Process

A simple look at the process of understanding the real world using network visualizations

World

Network

Display

Visualization

Understanding

Revises User Model and Action

Utility

Outline

Random Thoughts







6REALITY DISPLAY COGNITIVE MODEL

A First Step At QuantificationCan we quantify the continual process of the human world model

converging to reality via understanding gained from visualizations?

OldCognitive Model

RealityExploratory mode:1. The viz presents some bits of

reality to the user…some correctly, some not and some inadvertently via user induction

2. The user has a set of bits that represents their belief (their model of reality)

3. The impact of the viz is the replacement/modification of some of these bits

4. Based on the revised model, the user revises the utility of the set of available actions…hopefully this optimizes

Display

New Cognitive

Model

Visualization

Mental Processes

(Perception, etc)

Outline

Random Thoughts







7

Can The User Extract Information From A Hypernode?

Not necessarily as such when the links between hypernodes are determined by the component links of the sub-nodes

BUT – we might try grouping entities into hypernodes by various measures and THEN allowing links and structure to emerge between those hypernodes

Links among independent hypernode layers indicate pragmatically identical entities

Outline

Random Thoughts






Towards Quantification of Network Visualizations

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NAIVE ENTROPY IS BLIND TO THE USER

Measuring The Information Content In An Image In the image processing world, task-based experiments wherein

analysis are asked to perform a detection, decision or characterization task using an image [e.g. a tank in a field, etc]

In these experiments, information content in the image is measured by the entropy in the image

This entropy is a pixel based measure

Pixel based measures are too simplistic for measuring the information content in a visualization because they ignore the user’s perception and pragmatics

However, these methods can be tailored to measure the information content in a visualization

Outline

Random Thoughts




http://www.sxc.hu/browse.phtml?f=download&id=71221

http://www.sxc.hu/browse.phtml?f=download&id=71221




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IC[V] U f(N,L,l) and/or f( U(N),U(L),U(l))

Measuring The Information Content In A VisualizationInformation_Content(Visualization)

- Entropy_Aggregation(Nodes,Links,Labels,…) A visualization at any one point in time is an image used by the analyst

to perform a task

We can calculate the entropy of the visualization, taking into account pragmatic weightings on nodes based on various factors

Node/link based measures instead of pixel based measures

Weight nodes/links based on relevancy to the query or other pragmatic measures

Calculate the entropy of the visualization image at that point in time

Let the USER dial up and down the total entropy [aka information content] of the image to their own optimal level for that query at that moment.

Outline

Random Thoughts







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CONTINUOUS INTERACTIVE VISUALIZATION TUNING

User Interaction and Optimization With Information Theory

USER-CENTRIC OPTIMIZATION Since visualization is a personal experience, let the user tune their

visualization in a continuous, interactive way:

Increase/decrease the relevancy/attention given to certain types of nodes or links

Dial up and down the total entropy [aka information content] of the image to their own optimal level for that query at that moment.

The software will need to iterate an optimization program to:

Predict the entropy in a given layout of the network Reduce or increase entropy accordingly Create the layout Measure again Reduce or increase as necessary and so on All of this on the fly as the user is tuning their preferences

Outline

Random Thoughts




Sources




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ONE EXAMPLE

Tuning The SourcesSuppose an analyst has a network visualization at hand and is searching a corpus of documents or other sources to extract additional network

information

Each document or source will return a small sub-network

Compute the entropy difference between the existing network visualization and each source’s contribution.

Allow the analyst to dial up and down the number and types of sources to be merged into the visualization

Outline

Random Thoughts







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…SHANNON’S LAST THEOREM?

Conclusions

IDEAL OPTIMIZATION: minimize entropy and maximize utility

The user holds the definition and measure of utility within their mind and thus must contribute this measure via interaction with the system

Information theoretic optimization of visualization requires forms of user modeling/interaction

Outline

Random Thoughts




amy vanderbilt ~ vin taylor ~ martin taylor ~ mark nixon ~ jan terje bjorke

Documents

information content

information theorytuning

model revision5visualization

network visualizationshow

network visualizations3amy

network visualizations5amy

visualisation network

human world model