Transcript Exploring social networks with Matrix

Exploring social networks with
Matrix-based representations
Nathalie Henry
Co-supervisors:
Jean-Daniel Fekete & Peter Eades
[email protected]
Exploring social networks with matrices
Background
Peter Eades,
Univ. of Sydney
Sociologists
from EHESS,
Historians from the
French Archives,
Analysts from EDF
and France telecom
Jean-Daniel Fekete,
INRIA
[email protected]
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Social Networks
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What is it?
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A set of actors connected by relations
a HOT topic
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Online communities (FaceBook, Flickr)
Online collaboration (Wikipedia, Sourceforge)
Scientific collaboration
And more…
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FaceBook Interactive Graph
Internet Traffic [Eick et al.]
The WEB
Diseases transmission networks
Terrorists networks
[email protected]
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Analyzing social networks
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Answering questions with statistics
Exploratory Data Analysis [Tukey,77]
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Answering question you did not know you had
Looking at the raw data from different perspectives
Anscombe’s numbers
[email protected]
Statistics
Visual representation
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Network Visualization
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What social scientists start with:
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Node-Link Diagram
 Overlapping nodes
 Edge-crossing
 Identify connections
100+ actors - InfoVis community
[email protected]
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Network Visualization
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What social scientists end with:

[Ke et al.,04]
[email protected]
Most important tasks
 Communities
 Central Actors
 Discover the
structure of the
network
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Large Network Visualization
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What social scientists start with:

Node-Link Diagram
 Overlapping nodes
 Edge-crossing
 Identify connections
4000+ actors
[email protected]
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Large Network Visualization
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What social scientists end with:
4000+ actors
[email protected]
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What are the solutions?
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A year of emails
Sampling, filtering
Clustering into meta-nodes
Alternative representations such
as adjacency matrices
A
A
C
B
[email protected]
B C D
1
1
B 10
0 1
0
C 10
10
D 10
0 10
A 0 1
D
0 1
0
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My Approach
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Participatory Design
Observation
Evaluation
Brainstorming
Prototyping
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Outcomes
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They use statistics, graph analytics
They use mainly node-link diagrams
But they use sometimes matrices
[email protected]
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My Approach
Perception
Exploration
Information Visualization
Communication
[email protected]
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Itinerary of my PhD
[Survey in progress]
1. Make
matrices usable
3. Augment
matrices
[Henry and Fekete, IHM06&Infovis06]
2. Combine
matrix+node-link
4. Merge
matrix+node-link
[Henry and Fekete, Interact07]
Interaction technique
[Elmqvist et al., CHI08]
[Henry et al., Infovis07]
Case study [Henry et al., IJHCI07]
[email protected]
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Outline
Exploration
Perception
1. Make
matrices usable
3. Augment
matrices
2. Combine
matrix+node-link
4. Merge
matrix+node-link
Communication
[email protected]
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1. How to make matrices usable?
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J. Bertin, 1967
High-level structure?
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Groups
Trends
Outliers
 Make them VISUAL
 REORDER their rows and columns
[email protected]
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Reordering methods
[Survey to be published]
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Lot’s of methods !
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Table-based ordering methods
Graph linearization
Mine! (mixed approach)
A B C D E F G H I
Graph Linearization
[email protected]
J
Hierchical clustering
of microarray data
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Mixed approach
[Henry and Fekete, IHM’06]
[Henry and Fekete, InfoVis’06]

Place actors with similar connection
patterns beside each other
ABCDEFGH
B
E
H
A
C
F
D
G
A
B
C
D
E
F
G
H
1
0
0
0
0
0
0 0 0 1
0 0 0 0
0
1
1
1
0
0
1 1
0 0
0 0
0 0
1 0
0 1
0 0 0
1 0 0
0 1 0
0 0 1
0 0 0
0 0 0
0
0
0
0
1
1
0 0 0 0
1 1 0 0
ABCDEFGH
A
B
C
D
E
F
G
H
1
2
2
0
3
3
2 3 3 1
3 2 2 4
0
1
1
1
2
2
1 1
0 2
2 0
2 2
1 3
3 1
2 2 2
1 3 3
3 1 3
3 3 1
0 2 4
2 0 4
3
2
2
4
1
1
4 4 0 5
1 1 5 0
 Add information to the adjacency matrix
[email protected]
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Reordering methods
There are lots of methods…
… but the real question is:
 What is a GOOD ordering?
[email protected]
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What is a good ordering?
… for performing the analysis of a social network
Most important
tasks
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Communities:
B and C
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Central Actors:
A
Manual ordering
[email protected]
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What is a good automatic ordering?
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Algorithms designed from formal measures
Can we characterize them according to the
visual features they produce?
[email protected]
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Empirical study
[Henry and Fekete, Beliv’06]
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How people
perceive groups?
QuickTime™ and a
decompressor
are needed to see this picture.
[email protected]
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Is there A good ordering?
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For a specific
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visual feature?
type of data?
No :(
But it saves them time to start from somewhere!
Analysts need several orderings to find a consensus in the
data
 I focused on assisted reordering
and interactions to find consensus
[email protected]
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Interacting to find a consensus
[Henry and Fekete, IHM’06]
[Henry and Fekete, InfoVis’06]
> interactive (fuzzy) clustering
> interactive ordering
[email protected]
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Outline
Exploration
Perception
1. Make
matrices usable
3. Augment
matrices
2. Combine
matrix+node-link
4. Merge
matrix+node-link
Communication
[email protected]
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2. Combining best of both worlds with MatrixExplorer
QuickTime™ and a
decompressor
are needed to see this picture.
[email protected]
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MatrixExplorer
[Henry and Fekete, IHM’06]
[Henry and Fekete, InfoVis’06]
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Coordinated views
Interaction to explore
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Our hypothesis:
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Matrix to explore
Node-link to communicate
Our observations:
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Node-link for certain tasks, matrix for others
Cognitively demanding to switch back and forth
To find a consensus, they use both representations
different layouts/orderings
[email protected]
and
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Exploring with matrix or node-link?
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Social networks
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Sparse (genealogical tree)  node-link
Large/Dense (a year of emails)  matrix
Why are users switching back and forth
when exploring large/dense networks?
[email protected]
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Matrices weakness
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The problem of path-related tasks[Ghoniem et al., 2005]
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Always possible on matrices…
… but tedious !
?
?
?
A
A
B 0
C 0
[email protected]
1
A
0 1 0
B
0 0 1
C
0 0 0
A 0 1
C
B
B C D
D
D 0
1
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Outline
Exploration
Perception
1. Make
matrices usable
3. Augment
matrices
2. Combine
matrix+node-link
4. Merge
matrix+node-link
Communication
[email protected]
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3. Augmenting matrices with MatLink
QuickTime™ and a
decompressor
are needed to see this picture.
[email protected]
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MatLink
[Henry and Fekete, Interact’07]
Best paper award
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Adding static links
Adding interactive links
For larger matrices…
[email protected]
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Mélange
[Elmqvist, Henry, Riche and Fekete, CHI’08]
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Folding the space between 2 or more points of focus
Works well for matrices and social networks tasks
QuickTime™ and a
decompressor
are needed to see this picture.
[email protected]
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Exploring with matrix or node-link?
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Social networks
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Sparse (genealogical tree)  node-link
Large/Dense (a year of emails)  matrix
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Small-world networks:
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A common category of social networks
 Globally sparse but locally dense
 node-link?  matrix?

[email protected]
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Outline
Exploration
Perception
1. Make
matrices usable
3. Augment
matrices
2. Combine
matrix+node-link
4. Merge
matrix+node-link
Communication
[email protected]
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Small-world
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The best representation?
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What is happening
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Inside communities?
Between communities?
[Auber et al., InfoVis’04]
[email protected]
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4. Merging matrix and node-link with NodeTrix
QuickTime™ and a
decompressor
are needed to see this picture.
[email protected]
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Interactive pen tablet
QuickTime™ and a
decompressor
are needed to see this picture.
[email protected]
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NodeTrix
[Henry et al., InfoVis’07]
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Explore
QuickTime™ and a
decompressor
are needed to see this picture.
[email protected]

Communicate
QuickTime™ and a
decompressor
are needed to see this picture.
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Actors in one or more communities
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Where to place them?
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Between communities?
In one or the other community?
In overlapping communities?
In a higher level community?
Why not duplicating them?
What are the effect of duplication
on user understanding?
[email protected]
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Node Duplication
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More accurate community view
Minimizing the misleading effect
by visualizing links between duplicates
[email protected]
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What about evaluation?
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Participatory design
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Controlled experiment
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For specific tasks (low-level) on specific datasets
with specific representations
Case Study
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Mostly informal and during the whole process
More realistic settings
Longitudinal Study… coming
[email protected]
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Case Study on 20 years of 4 HCI conferences
[Henry et al., IJHCI 07]
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Over 5 months
Exploratory analysis of
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Using MatrixExplorer
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332 conferences
27 000 actors
118 000 relations
Exploring large networks
Presenting the results
Then MatLink and NodeTrix
[email protected]
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Communicating information
InfoVis coauthorship
[email protected]
AVI coauthorship
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Communicating information (2)
UIST coauthorship poster
[email protected]
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What I have done
Exploration
Perception
1. Make
matrices usable
3. Augment
matrices
2. Combine
matrix+node-link
4. Merge
matrix+node-link
Communication
[email protected]
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Research directions
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Time-related data
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On social networks
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On other data
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Scientific collaboration (INRIA)
Group awareness (Wikipedia France)
Biological networks (Institut Pasteur)
Reflecting on user activity (MSR)
Longitudinal and qualitative studies
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Logging data
New visualizations for
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analyzing the data collected
reflecting it to users: for exploration and communication
[email protected]
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La FIN!
I’m here!
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Research approach
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Participatory design
Several perspectives
Research directions
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Time-related data
Evaluation
[email protected]
INRIA in 2006
156 teams,
8400 persons
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