Causality Visualization Causality Visualization Using Animated Using Animated Growing PolygonsGrowing Polygons

Niklas Elmqvist (elm@cs.chalmers.se)

Philippas Tsigas (tsigas@cs.chalmers.se)

IEEE 2003 Symposium on Information VisualizationOctober 19th-21st, Seattle, Washington, USA.

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Outline

Introduction and Motivating Example

Related Work

The Growing Polygons Technique

User Study & Results

Conclusions & Future WorkRoadmap

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Introduction

The concepts of cause and effect are pervasive in human thinking

Causality is a very important reasoning tool in both science as well as everyday life

Causal relations can be very complex This talk describes effective ways of visualizing causality

”Since we believe that we know a thing only when we can say why it is as it is—which in fact means grasping its primary causes (aitia)—plainly we must try to achieve this [...] so that we may know what their principles are and may refer to these principles in order to explain everything into which we inquire.”

-- Aristotle, Physics II.3.

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Example: Citations

Let’s study the chain of citations in a collection of scientific papers A citation can be seen as an influence Citation graphs can be very large

Studying these chains can give the following information How are authors are influenced by other

authors? How are ideas propagated in a scientific

community?

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Example: Citations (2)

time

Author A

Author B

Author C

1999 2000 2001 2002 2003

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Causality Visualization

Formally, we are looking to visualize systems of causal relations Def: The causal relation is a relation that connects

two elements (events) x and y as x y iff x influences y.

Sets of events are called processes P1,..., PN

• Internal events are sequential and causally related

• External events interconnect processes through messages

Effective visualization is a difficult problem Traditional visualization: Hasse diagrams

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Applications

General information flow problems Rumor spreading Citation networks Software visualization

Learning, designing, or debugging distributed programs and algorithms

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Related Work: Hasse Diagrams Distributed system with n=20

processes and 60 system events Difficult to comprehend

Intersecting and coinciding message arrows

Fine granularity The user must manually maintain ”the

context” of the relations Users may have to backtrace every

single message Vital information is scattered

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Related Work: Growing Squares

Our earlier attempt at improving causality visualization

Processes represented by animated 2D squares

Presented at SoftVis 2003 More efficient than Hasse

diagrams but: Similar colors reduce scalability Influences are ”mixed up” No absolute timing information

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Growing Polygons Refinement of Growing

Squares Idea: Represent each

process by an n-sided polygon (process polygon) Assign each process a unique

color Assign each process a unique

triangular sector in the polygons

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Growing Polygons (2) Process polygons are laid out on

a large n-sided layout polygon Each polygon grows as time

progresses Animated timeline

Messages are shown as arrows travelling from one process to another at specific points in time Messages carry influences (see next

slide)

Simplified GP diagram

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Growing Polygons: Influences

Messages carry influences (causal relations) Source color is

transferred to the destination

Causal relations are also transitive Transitive ”colors” are

also carried across Both color and

orientation used for separating processes

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Growing Polygons: Example (1)

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Growing Polygons: Example (2)

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Growing Polygons: Example (3)

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Growing Polygons: Example (4)

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Growing Polygons: Example (5)

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Hasse vs Growing Polygons

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User Study

A formal user study comparing Hasse diagrams to Growing Polygons was performed Two-way repeated-measures ANOVA Independent variables (both within-subjects):

• Visualization type: Hasse or GP

• Data density: sparse and dense

4 different data sets: 1 of each data density for each visualization type

20 subjects participated in the test All subjects knowledgeable in distributed systems

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User Study: Tasks

Each data set required the user to solve 4 common questions related to causal relations:

1. Find the process with longest duration

2. Find the process that has had the most influence on the system

3. Find the process that has been influenced the most

4. Is process x causally related to process y?

Times were measured for these tasks Users were also asked for their subjective

opinion of the visualization (rating and ranking)

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Results

Performance measurement Users were more efficient using

Growing Polygons than Hasse diagrams

• Hasse: 434 (s.d. 379) seconds • GP: 252 (s.d. 175) seconds

This is a significant difference for both sparse and dense densities

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Results (2)

Correctness Users are more correct

when solving problems using Growing Polygons than Hasse diagrams

• Hasse: 4.4 (s.d. 1.1) correct• GP: 5.6 (s.d. 0.7) correct

This was a significant difference for both sparse and dense densities

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Results (3)

Subjective ratings Very positive user feedback Users consistently rated GP over Hasse diagrams in

all respects (ease-of-use, enjoyability, efficiency) These readings were all statistically significant The majority of users also rated GP over Hasse GP Hasse Don't know

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Conclusions & Future Work

Visualization of causal relations is crucial for understanding complex systems

Traditional visualization techniques (Hasse diagrams) fall short

Growing Polygon is a novel idea of visualizing causality focused on the information flow

Our visualization technique is Significantly more efficient to use than Hasse diagrams Significantly more appealing to users than Hasse diagrams

In the future we want to explore scalability concerns in systems spanning long time periods and involving many processes

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Questions?

Contact information: Address:

Niklas Elmqvist and Philippas Tsigas

Department of Computing Science

Chalmers University of Technology

SE-412 96 Göteborg, Sweden

Email:• {elm|tsigas}@cs.chalmers.se

Project website:• http://www.cs.chalmers.se/~elm/projects/causalviz