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COMP5048

Information Visualisation

2011 Semester 2

Tuesday 3-5pm

SIT Lecture Theater

http://www.it.usyd.edu.au/~shhong/comp5048.htm

Lecturer: Seokhee Hong (shhong@it.usyd.edu.au)

Consultation: Tuesday 5-6pm

COMP5048 Course Outline

Unit Specification

Assumed Knowledge

Learning Outcomes

Assessment

USYD Policies

Lecture Time Table

References

Unit Specification

Information Visualisation aim to make good pictures of abstract information, such as stock prices, computer networks, social networks and software diagrams.

The main challenge is to design and implement effective and efficient algorithms that produce good pictures of abstract data.

The unit will review basic concepts, techniques and

fundamental algorithms to achieve good

visualisation of abstract.

Assumed Knowledge

Basic Knowledge in Algorithms/Data Structures

Basic Skills in Programming

Learning Outcomes

understanding of basic concepts, techniques and

algorithms for good visualisation of abstract data

using efficient algorithms to produce good

visualisation of abstract data

applying effective visualisation methods for

specific application area

Assessment

Week 7: Assignment 1 (10%)

Week 11-13: Presentation (10%)

Programming Assignment (40%) - group work

• Week 9: Report 1 (10%)

• Week13: Report 2/demo/presentation (30%)

Exam: 40%

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USYD/SIT Policies

You are required to carefully read the policies on

• Academic Honesty and Plagiarism

• Special consideration due to illness and

misadventure

• No late submissions allowed

Topics Covered: Week 1-7

Approximate schedule: topics are subject to change

Week 1 (SH): Introduction

Week 2 (SH): Tree Drawing

Week 3 (PE): Spring Algorithm

Week 4 (PE): Drawing Clustered Graphs

Week 5 (SH): Network Analysis

Week 6 (SH): Visual Analytics

Week 7 (SH): 3D and Interaction

Topics Covered: Week 8-13

Approximate schedule: topics are subject to change

Week 8 (FF): Sugiyama Method

Week 9 (TH): Evaluation

Week 10 (FF): Drawing Planar Graphs

Week 11: Student Presentation

Week 12: Student Presentation

Week 13 : Student Presentation

References Giuseppe Di Battista, Peter Eades, Roberto Tamassia, Ioannis G. Tollis,

"Graph Drawing: Algorithms for the Visualization of Graphs", Prentice-Hall, 1999.

Michael Kaufmann, Dorothea Wagner eds., “Drawing Graphs - Methods and Models”, Springer-Verlag, Lecture Notes in Computer Science, vol. 2025, 2001.

Readings in Information Visualization : Using Vision to Think, Stuart Card, Jock Mackinlay, & Ben Shneiderman, Morgan Kaufmann, 1999.

Information Visualization : Perception for Design, Colin Ware, Morgan Kaufmann, 2000.

Information Visualization by Robert Spence, Pearson Addison Wesley, 2000

Conference Proceedings: GD, IEEE InfoVis, EuroVis, VAST, PacificVis

Journals:

• Information visualization, www.palgrave-journals.com/ivs/

• IEEE Transactions on Visualization and Computer Graphics,

http://www.computer.org/tvcg/

Lecture 1

Information Visualisation

and

Graph Drawing

Visualisation

Visualisation:

the use of computer-supported, interactive,

visual representations of data to amplify

cognition.

• Scientific visualisation:

the use of computer-supported, interactive,

visual representations of scientific data to

amplify cognition.

• Information visualisation:

the use of computer-supported, interactive,

visual representations of abstract data to

amplify cognition.

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Astrophysics - Astronomy

Visualisation of the Durham/UKST

Galaxy Redshift Survey

Andrew Ratcliffe, Physics,

University of Durham, U.K

Chemistry – Biochemistry

Molecular Modelling of

Immunosuppressant Molecules Bound

to an Enzyme

Peter Karuso, School of Chemistry,

Macquarie University

Scientific Visualisation Chemistry

- Biochemistry

Molecular Modelling Animation

Peter Karuso, School of

Chemistry, Macquarie University

Geophysics

Fly Thru of the Bathymetric Data

obtained for East Bass Strait

Ben Simons, Sydney VisLab/ Chris

Jenkins, Jock Keene, Dept of Geology

and GeoPhysics, University of

Sydney

Information Visualisation the loss of Napoleon’s army

Edward R. Tufte, The Visual Display of Quantitative Information

by Charles Joseph Minard (1781-1870)

Russian-Polish border 422,000 men / Moscow 100,000 men.

Information Visualisation

Abstract Data Picture

Information visualisation research aims to make

pictures of abstract data so that humans can

understand, navigate, and manipulate the data.

Good visualisation

• H. Beck

Bad visualisation

ALP

4

Data Knowledge Picture

analysis visualisation

Graph Graph drawing

Visualisation of Abstract Information

There are two steps:

1. Analysis: extracting a graph from the information

2. Visualisation: Graph drawing

Visualisation of football transfers

Drew moved from the

Panthers to the Eels

Miles moved from the

Roosters to the Eagles

Green moved from the

Cowboys to the

Roosters

O’Hara moved from the

Bulldogs to the Raiders

. . . . . .

Visualisation Analysis

Data Graph Picture

Drew moved from the Panthers to the

Eels

Miles moved from the Roosters to the

Eagles

Green moved from the Cowboys to the

Roosters

O’Hara moved from the Bulldogs to the

Raiders

. . . . . .

Analysis

Data Graph

Visualisation

Graph Picture

Drew moved from the

Panthers to the Eels

Miles moved from the

Roosters to the Eagles

Green moved from the

Cowboys to the Roosters

O’Hara moved from the

Bulldogs to the Raiders

. . . . . .

Visualisation of Social Networks

email friends

Email log files reflect relationships between people

Definition: friends network

– X and Y are email friends if

– X sends more than 5 messages per day to Y,

and

– Y sends more than 5 messages per day to X.

The email_friends graph can be derived from email log files.

X

Email_friends ( X )

Mary Peter, Albert, DavidF, Alan

Judy Bob, Alan

Peter Mary, DavidF, Jon

DavidF Albert, Joseph, Peter, Mary

Jon Peter, Joseph, DavidE

DavidE Jon, Joseph, Albert

Joseph DavidE, Jon, DavidF

Bob Judy, Alan

Alan Bob, Mary, Judy

Albert DavidF, Mary, DavidE

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Peter

Mary

Jon

DavidF

Alan

Joseph

Bob

Judy

Albert

DavidE

Bad Visualisation

why?

Peter

Mary

Jon

DavidF

Alan

Joseph

Bob Judy

Albert

DavidE

Good Visualisation

why?

Email

Log

Files

There are two steps to visualising graphs:

1. Analysis: extracting a graph from the information

2. Graph drawing

Person X

Main email mates of X

Mary Peter, Albert, Judy, DavidF

Judy Mary, Bob, Alan

Peter Mary, David, Jon

David Albert, Joseph, Bob

Jon Peter, Joseph, Alan

David Peter, Joseph, Albert

Joseph David, Jon, David

Bob Judy, Alan, David

Alan Bob, Jon, Judy

Albert David, Mary, David

Peter

Mary

Jon

DavidF

Alan

Joseph

Bob Judy

Albert

DavidE

2. G

rap

h d

raw

ing

1. A

naly

sis

Reference Model for Visualisation

Raw

Data

Data

Table

Visual

Structures Views

Data Visual Form

Human Interaction

Data

Transformations

Visual

Mappings

View

Transformations

[Johnson, Shneiderman 91] Treemaps: A Space-filling

Approach to the Visualization of Hierarchical Information

Tree Maps

• [Robertson, Mackinlay, Card, CHI 91]

"Cone Trees: Animated 3D visualizations of hierarchical information.

Cone Tree

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2D Hyperbolic Tree Browser

[Lamping, Rao, Pirolli, CHI’95] A Focus+Context Technique Based on Hyperbolic Geometry for Visualizing Large Hierarchies.

Distortion & Hierarchy M.C. Escher, Circle Limit IV (Heaven and Hell)

H3: 3D Hyperbolic

data: web hyperlinks

• quasi-hierarchical

graphs: can find

reasonable spanning tree

using domain-specific

information

goal: scalability

H3: Laying Out Large Directed Graphs in 3D Hyperbolic Space

Tamara Munzner, proc. of IEEE Symposium on Information Visualization97

Walrus - Graph Visualization Tool

Directory Trees

Focus+Context (FishEye View)

the perspective wall (Mackinlay, Robertson and Card, 1991)

• [Becker, Eick, Wilks 95]

• SeeNet [Cox, Eick 95] 3D Displays of

Network Traffic

SeeNet3D

Network Data Visualisation

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Visualizing the Topology of the MBone

time: 1996

data: MBone tunnels

task: find badly placed tunnels

goal: simple baseline

method: 3D geographic

Visualizing the Global Topology of the MBone

Tamara Munzner and Eric Hoffman and K. Claffy and Bill Fenner

Proceedings of the 1996 IEEE Symposium on Information Visualization, SeeSoft

Software Visualisation

Database Visualisation

VISDB

Five-dimensional artificially generated data set (100,000 points) in simple configuration.

NicheWorks: Exploring Large Networks

NicheWorks - Interactive Visualization of Very Large Graphs, by Graham J Wills.

Typical analyses performed using NicheWorks have between 20,000 and 1,000,000 nodes.

Example

Overview of calling patterns

International Calling Fraud

40,000 calls involving 35,000 callers

Example

High users' calling patterns

International Calling Fraud

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International Calling Fraud

possible fraud pattern

The Israel-Jordan-UAE generated subset

zooming in to those callers calling more than

one country

SPIRE

SPIRE-Spatial Paradigm for Information

Retrieval and Exploration

SPIRE provides a wealth of tools for exploring

the information, including query, subset, and

trend analysis tools.

http://www.pnl.gov/infoviz/spire/spire.html

Pacific Northwest National Laboratory, USA

Galaxies Galaxies The Galaxies visualization uses the image of

stars in the night sky to represent a set of

documents.

Each document is represented by a single

"docustar."

Closely related documents cluster together

while unrelated documents are separated by

large distances.

Several analytical tools are provided with

Galaxies to allow users to investigate the

document groupings, query the document

contents, and investigate time-based trends.

ThemeView ThemeView

The topics or themes within a set of documents are

shown as a relief map of natural terrain.

The mountains in the ThemeView indicate dominant

themes.

The height of the peaks indicates the relative strengths

of the topics in the document set.

Similar themes appear close together, while unrelated

themes are separated by larger distances.

ThemeView provides a visual overview of the major

topics contained in a set of documents.

Combined with its exploration tools, ThemeView

permits the analyst to identify unanticipated

relationships and examine changes in topics over time.

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CAIDA

CAIDA, the Cooperative Association for

Internet Data Analysis, provides tools and

analyses promoting the engineering and

maintenance of a robust, scalable global

Internet infrastructure.

http://www.caida.org

The AS Internet graph One of CAIDA's skitter project goals is to develop

techniques to illustrate relationships and depict critical components of the Internet infrastructure.

The graph reflects 626,773 IP addresses and 1,007,723 IP links of skitter data from 16 monitors probing approximately 400,000 destinations spread across over 48,302 (52%) of globally routable network prefixes.

Then aggregate this view of the network into a topology of Autonomous Systems (ASes), each of which approximately maps to an Internet Service Provider (ISP).

The abstracted graph consists of 7,624 Autonomous System (AS) nodes and 25,126 peering sessions.

The AS Internet graph

A Macroscopic Visualisation of the Internet During October, 2000 Internet Mapping Project

Bill Cheswick, Bell Labs and Hal Burch, CMU

a long-term project to collect routing data on the Internet.

This mapping consists of frequent traceroute-style path probes, one to each registered Internet entity.

They build a tree showing the paths to most of the nets on the Internet.

These paths change over time, as routes reconfigure and the Internet grows.

They are preserving this data to show how the Internet grows.

layout showing the major ISPs.

Graph Drawing

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Hofstadter. Godel, Escher, Bach. [Gansner and North]

improved force-directed layouts.

Graph Drawing Graph Drawing

Graphs are abstract structure that are used to

model relational information.

Graph G=(V,E)

•V: set of vertices (objects)

•E: set of edges connecting vertices(relationship)

Graph Drawing: automatic construction of

geometric representations of graphs in 2D or 3D.

Graph Drawing

The classical graph drawing problem is to develop algorithms to draw graphs.

A - B, C, D

B - A, C, D

C - A, B, D, E

D - A, B, C, E

E - C, D

The input is a graph

with no geometry

A B

D

C

E

The output is a drawing of the

graph; the drawing should be easy

to understand, easy to remember,

beautiful.

A

B

C

D

E

file edit insert layout

Graph Drawing

agnt(monkey, eat).

inst(eat, spoon).

obj(eat, walnut).

part_of(walnut, shell).

matr(spoon, shell).

The graph drawing problem is to design methods to give

good drawings of graphs.

monkey

eat

spoon shell

walnut

agnt

inst

obj

part_of

matr

The input graph is usually

some relational

description of a software

system.

The output picture is used in a system

design/analysis tool.

_ X

Tangled drawing

Untangled

drawing

Applications

Software engineering

Database

Information system

Realtime system

Computer Network

VLSI

AI

Data Mining

Bioinformatics

Decision support system

Biology

Chemistry

…

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Graph

Drawing VLSI

- circuit schematics

Decision Support System

- Pert network

Real-time System

- Petri nets

- state transition

diagrams

Software Engineering

- data flow diagram

- subroutine call graph

- program nesting trees

- object oriented class

hierarchy

Information System

- organization

charts

Data base

- entity relationship

diagram

Artificial intelligence

- knowledge

representation

diagram

Graphs

tree

• free tree

• binary tree

• rooted tree

• ordered tree

planar graphs

general graphs

directed graphs

extended graph model

• hierarchical graphs

• clustered graphs

• hyper graphs

• higraphs

Drawing conventions

polyline drawing

straight-line drawing

orthogonal drawing

grid drawing

planar drawing

upward drawing

convexity

…

Orthogonal drawing

bend

Straight-line drawing

Polyline drawing Upward drawing

Drawing conventions

monkey eat

spoon

shell

walnut

agnt

inst

obj

part_of

matr

less readable

monkey

eat

spoon shell

walnut agnt

inst

obj

part_of

matr

more readable

readability: the drawing should be easy to read,

easy to understand, easy to remember,

beautiful.

Aesthetics Aesthetics

crossings

area

symmetry

edge length

• total edge length, maximum edge length, uniform edge length

bends

• total bends, maximum bends, uniform bends

angular resolution

aspect ratio

Readability is sometimes measured by aesthetic criteria

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Avoid

edge crossings

Avoid

edge bends

Avoid

long edges

monkey eat

spoon

shell

agnt

inst part_of

matr

less readable

walnut

obj

Crossings and bends

monkey eat

spoon

shell

walnut

agnt

inst

obj

part_of

matr

One should spread the

nodes evenly over the page.

This can be measured:

• minimise area (for fixed

size nodes)

or equivalently

• maximise resolution

(for a fixed size screen).

Area and resolution

• minimum edge

crossings,

• minimum bends,

• minimum edge lengths,

• maximum resolution,

and many more.

monkey

eat

spoon shell

walnut agnt

inst

obj

part_of

matr

more readable

There are many aesthetic criteria for good diagrams:

Aesthetics NP-hardness

minimize crossings

minimize area

maximize symmetry

minimize total edge length

minimize number of bends

maximize angular resolution

…

Conflicts

Minimize

edge

crossings

Maximize

symmetry

Graph Drawing Algorithms

Tree Drawing

•Tidy drawing

•Free tree drawing

Planar Graphs

•Straight-line drawing

•Orthogonal (grid) Drawing

Undirected graphs: Spring algorithm (force

directed methods)

Directed graphs : Sugiyama method

(Layered/Hierarchical drawing)

Clustered graphs …

Latour tree visualization system

I. Herman, G. Melançon, M.S. Marshall,VisSym99

Radial layout of

29773 nodes Reingold-Tilford layout of

3255 nodes

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73 Maolin Huang

On-line Graph Navigation System Graphviz, AT&T, USA

GDToolkit, Italy

Hermes: internet topology

OGDF, Germany

Hierarchical layout Symmetric layout Circular layout

Tom Sawyer Software, USA

titletitle

Social network

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Days of Thunder (1990)

Far and Away (1992)

Eyes Wide Shut (1999)

A Few Good Man

Kevin Bacon Number 1 Kevin Bacon Number ２

Actor Collaboration Network

Important researchers (research groups) with their research area

Ahmed et al.: EuroVis 05 (IEEE InfoVis 2004 competition winner)

IEEE InfoVis Research

Evolution of

research area

Visualisation of FIFA 2002 World Cup

Ahmed, Fu, Hong, Quan, Xu: GD 2006 competition winner

Visualisation of Stock Market Data

Tim Dwyer

Visualisation of

Fund Manager

Movement Graph

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Visualisation of Patterns – motif “Motifs”: small pattern in the network which occurs with significantly high

frequency.

Data: transcriptional regulation network of Escherichia coli.

Visualisation and Analysis of Network Motifs (IV 2005)

W. Huang, C. Murray, X. Shen, L. Song, Y. Wu, L. Zheng.

Map of protein-protein interactions. The colour of a node signifies the phenotypic

effect of removing the corresponding protein (red, lethal; green, non-lethal;

orange, slow growth; yellow, unknown). By Hawoong Jeong

Visualization

of

Protein Interaction

Network

Visualization of

Biochemical

Pathways

Falk Schreiber

Homework

Examples of

Good Visualisation

Bad Visualisation

send me a ppt slide with pictures

+ source: data, paper, author, website

by next monday