Visualisation Network-of-Experts Malvern, UK NOV 4-6th 2008 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) Information-Theoretic Considerations of Graph/Network Topology 2 Outline Random Thoughts Cognitive Model Revision Effect of Hypernodes Quantification of Network Visualizations  How Does Information Theory Support Visualization? – Random Philosophical Thoughts  Cognitive Model Revision  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 

Amy K. C. S. Vanderbilt, Ph.D. TITLE (USA) HOW DOES INFORMATION THEORY SUPPORT VISUALIZATION? 3 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 Cognitive Model Revision Effect of Hypernodes Quantification of Network Visualizations

Amy K. C. S. Vanderbilt, Ph.D. TITLE (USA) HOW DOES INFORMATION THEORY SUPPORT VISUALIZATION? 4 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 Cognitive Model Revision Effect of Hypernodes Quantification of Network Visualizations

Pragmatic Context  User Syntax & Semantics  Data System Happens in the User’s Mind Amy K. C. S. Vanderbilt, Ph.D. TITLE (USA) Cognitive Model Revision 5 VISUALIZATION 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 Cognitive Model Revision Effect of Hypernodes Quantification of Network Visualizations

Amy K. C. S. Vanderbilt, Ph.D. TITLE (USA) Cognitive Model Revision 6 REALITY  DISPLAY  COGNITIVE MODEL A First Step At Quantification Can we quantify the continual process of the human world model converging to reality via understanding gained from visualizations? Old Cognitive Model Reality Exploratory 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 Cognitive Model Revision Effect of Hypernodes Quantification of Network Visualizations

Amy K. C. S. Vanderbilt, Ph.D. TITLE (USA) Effect of Hypernodes 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 Cognitive Model Revision Effect of Hypernodes Quantification of Network Visualizations

Amy K. C. S. Vanderbilt, Ph.D. TITLE (USA) Towards Quantification of Network Visualizations 8 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 Cognitive Model Revision Effect of Hypernodes Quantification of Network Visualizations

Amy K. C. S. Vanderbilt, Ph.D. TITLE (USA) Towards Quantification of Network Visualizations 9 IC[V]  U f(N,L,l) and/or f( U(N),U(L),U(l)) Measuring The Information Content In A Visualization Information_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 Cognitive Model Revision Effect of Hypernodes Quantification of Network Visualizations

Amy K. C. S. Vanderbilt, Ph.D. TITLE (USA) Towards Quantification of Network Visualizations 10 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 Cognitive Model Revision Effect of Hypernodes Quantification of Network Visualizations

Sources Amy K. C. S. Vanderbilt, Ph.D. TITLE (USA) Towards Quantification of Network Visualizations 11 ONE EXAMPLE Tuning The Sources Suppose 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 Cognitive Model Revision Effect of Hypernodes Quantification of Network Visualizations

Amy K. C. S. Vanderbilt, Ph.D. TITLE (USA) Towards Quantification of Network Visualizations 12  …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 Cognitive Model Revision Effect of Hypernodes Quantification of Network Visualizations