Download COMP5048 Information Visualisation COMP5048 Course Outline

COMP5048
Information Visualisation
COMP5048 Course Outline
 Unit Specification
 Assumed Knowledge
2011 Semester 2
 Tuesday 3-5pm
 SIT Lecture Theater
 http://www.it.usyd.edu.au/~shhong/comp5048.htm
 Lecturer: Seokhee Hong ([email protected])
 Consultation: Tuesday 5-6pm
Unit Specification
 Information Visualisation aim to make good pictures
of abstract information, such as stock prices,
computer networks, social networks and software
diagrams.
 Learning Outcomes
 Assessment
 USYD Policies
 Lecture Time Table
 References
Assumed Knowledge
 Basic Knowledge in Algorithms/Data Structures
 Basic Skills in Programming
 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.
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%
1
USYD/SIT Policies
Topics Covered: Week 1-7
 You are required to carefully read the policies on
• Academic Honesty and Plagiarism
• Special consideration due to illness and
misadventure
• No late submissions allowed
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
References
Topics Covered: Week 8-13

Giuseppe Di Battista, Peter Eades, Roberto Tamassia, Ioannis G. Tollis,
"Graph Drawing: Algorithms for the Visualization of Graphs", Prentice-Hall,
1999.
Approximate schedule: topics are subject to change

Michael Kaufmann, Dorothea Wagner eds., “Drawing Graphs - Methods and
Models”, Springer-Verlag, Lecture Notes in Computer Science, vol. 2025,
2001.
 Week 8 (FF): Sugiyama Method

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:
 Week 9 (TH): Evaluation
 Week 10 (FF): Drawing Planar Graphs
 Week 11: Student Presentation
 Week 12: Student Presentation
 Week 13 : Student Presentation
• Information visualization, www.palgrave-journals.com/ivs/
• IEEE Transactions on Visualization and Computer Graphics,
http://www.computer.org/tvcg/
Visualisation
Lecture 1
Information Visualisation
and
Graph Drawing
 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.
2
Scientific Visualisation
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
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
Information Visualisation
Abstract Data
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.
Good visualisation
Picture
Information visualisation research aims to make
pictures of abstract data so that humans can
understand, navigate, and manipulate the data.
Bad visualisation
• H. Beck

ALP
3
Visualisation of Abstract Information
Visualisation of football transfers
There are two steps:
1. Analysis: extracting a graph from the information
2. Visualisation: Graph drawing
visualisation
analysis
Data
Knowledge
Graph
Analysis
Data
Analysis
Picture
Graph
drawing
 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
 .... . .
Graph
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.
Data
 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
Graph
 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
 .... . .
Picture
Visualisation
Picture
Graph
 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
4
Bad Visualisation
Good Visualisation
Alan
Peter
Albert
Mary
DavidE
Joseph
Jon
Albert
DavidF
Peter
Bob
Mary
DavidF
Judy
Alan
Jon
Bob
Judy
DavidE
Joseph
 why?
 why?
There are two steps to visualising graphs:
1. Analysis: extracting a graph from the information
2. Graph drawing
Reference Model for Visualisation
Data
Visual Form
Main email mates of X
Person X
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
2. Graph drawing
1. Analysis
Email
Log
Files
Mary
DavidE
Joseph
Jon
Albert
DavidF
Peter
Mary
Raw
Data
Data
Table
Data
Transformations
Visual
Structures
Visual
Mappings
Views
View
Transformations
Alan
Judy
Tree Maps
[Johnson, Shneiderman 91] Treemaps: A Space-filling
Approach to the Visualization of Hierarchical Information
Bob
Human Interaction
Cone Tree
• [Robertson, Mackinlay, Card, CHI 91]
"Cone Trees: Animated 3D visualizations of hierarchical information.
5
2D Hyperbolic Tree Browser
 [Lamping, Rao, Pirolli, CHI’95] A Focus+Context Technique
Based on Hyperbolic Geometry for Visualizing Large
Hierarchies.
 Distortion & Hierarchy
H3: 3D Hyperbolic
 M.C. Escher, Circle Limit IV (Heaven and Hell)
Walrus - Graph Visualization Tool
 data: web hyperlinks
• quasi-hierarchical
graphs: can find
reasonable spanning tree
using domain-specific
information
Directory Trees
 goal: scalability
H3: Laying Out Large Directed Graphs in 3D Hyperbolic Space
Tamara Munzner, proc. of IEEE Symposium on Information Visualization97
Focus+Context (FishEye View)
Network Data Visualisation
• [Becker, Eick, Wilks 95]
• SeeNet
the perspective wall (Mackinlay, Robertson and Card, 1991)
 [Cox, Eick 95] 3D Displays of
Network Traffic
 SeeNet3D
6
Visualizing the Topology of the MBone
Software Visualisation
 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
Database Visualisation
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.
VISDB
Five-dimensional artificially generated data set (100,000 points) in simple configuration.
International Calling Fraud
Example
Overview of calling patterns
40,000 calls involving 35,000 callers
International Calling Fraud
Example
High users' calling patterns
7
International Calling Fraud
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.
possible fraud pattern
 The Israel-Jordan-UAE generated subset
 zooming in to those callers calling more than
one country
Galaxies
http://www.pnl.gov/infoviz/spire/spire.html
Pacific Northwest National Laboratory, USA
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.
8
The AS Internet graph
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
A Macroscopic Visualisation of the Internet During October, 2000
 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.
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 traceroutestyle 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
9
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.
Hofstadter. Godel, Escher, Bach.
[Gansner and North]
improved force-directed layouts.
Graph Drawing
Graph Drawing
 The classical graph drawing problem is to
develop algorithms to draw graphs.
The input is a graph
with no geometry
A - B, C, D
B - A, C, D
C - A, B, D, E
D - A, B, C, E
E - C, D
The output is a drawing of the
graph; the drawing should be easy
to understand, easy to remember,
beautiful.
C C
E
A
B B
The input graph is usually
some relational
description of a software
system.
The output picture is used in a system
design/analysis tool.
file edit insert layout
agnt
agnt(monkey, eat).
inst(eat, spoon).
obj(eat, walnut).
part_of(walnut, shell).
matr(spoon, shell).
eat
_
obj
inst
spoon
X
monkey
walnut
part_of
matr
shell
E
A
D D
The graph drawing problem is to design methods to give
good drawings of graphs.
Applications
Tangled drawing
Untangled
drawing
 Software engineering
 Database
 Information system
 Realtime system
 Computer Network
 VLSI
 AI
 Data Mining
 Bioinformatics
 Decision support system
 Biology
 Chemistry
…
10
Software Engineering
- data flow diagram
- subroutine call graph
- program nesting trees
- object oriented class
hierarchy
Information System
- organization
charts
Data base
- entity relationship
diagram
Graph
Drawing
Real-time System
- Petri nets
- state transition
diagrams
VLSI
- circuit schematics
Artificial intelligence
- knowledge
representation
diagram
Decision Support System
- Pert network
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
Drawing conventions
 polyline drawing
 straight-line drawing
 orthogonal drawing
 grid drawing
 planar drawing
 upward drawing
 convexity
…
bend
Straight-line drawing
Polyline drawing
Aesthetics
agnt
eat
obj
inst
part_of
walnut
matr
spoon
less readable
eat
obj
Readability is sometimes measured by aesthetic criteria
walnut
agnt
shell
monkey
Upward drawing
Aesthetics
readability: the drawing should be easy to read,
easy to understand, easy to remember,
beautiful.
monkey
Orthogonal drawing
inst
spoon
part_of
matr
more readable
shell
 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
11
Crossings and bends
Area and resolution
Avoid
edge crossings
monkey
eat
obj
agnt
shell
inst
part_of
walnut
matr
spoon
monkey
Avoid
edge bends
Avoid
long edges
eat
obj
agnt
shell
inst
part_of
walnut
matr
spoon
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).
less readable
Aesthetics
NP-hardness
There are many aesthetic criteria for good diagrams:
• minimum edge
crossings,
• minimum bends,
• minimum edge lengths,
• maximum resolution,
and many more.
agnt
monkey
eat
obj
inst
spoon
walnut
part_of
matr
 minimize crossings
 minimize area
 maximize symmetry
 minimize total edge length
 minimize number of bends
 maximize angular resolution
…
Conflicts
Minimize
edge
crossings
Maximize
symmetry
shell
more readable
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
12
On-line Graph Navigation System
Graphviz, AT&T, USA
Maolin Huang73
OGDF, Germany
GDToolkit, Italy
Hermes: internet topology
titletitle
Tom Sawyer Software, USA
Social network
Hierarchical layout
Symmetric layout
Circular layout
13
Actor Collaboration Network
A Few Good Man
Days of Thunder (1990)
Far and Away (1992)
Eyes Wide Shut (1999)
Kevin Bacon Number 1
Kevin Bacon Number ２
IEEE InfoVis Research
Important researchers (research groups) with their research area
Ahmed et al.: EuroVis 05 (IEEE InfoVis 2004 competition winner)
Evolution of
research area
Visualisation of FIFA 2002 World Cup
Visualisation of Stock Market Data
Visualisation of
Fund Manager
Movement Graph
Tim Dwyer
 Ahmed, Fu, Hong, Quan, Xu: GD 2006 competition winner
14
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.
Visualization
of
Protein Interaction
Network
Visualization of
Biochemical
Pathways
Falk Schreiber
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
Homework
Examples of
Good Visualisation
Bad Visualisation
send me a ppt slide with pictures
+ source: data, paper, author, website
by next monday
15