Slides of the tutorial "Computational Methods and Tools for Social Network Analysis Networked Learning Communities" at the LAK 2013 in Leuven. Tutorial Homepage: http://snatutoriallak2013.ku.de/index.php/SNA_tutorial_at_LAK_2013 Conference Homepage: http://lakconference2013.wordpress.com/

1. Computational Methods
and Tools for Social
Network Analysis of
Networked Learning
Communities
Tutorial at LAK 2013, 9/4/2013
Andreas Harrer, Tilman Göhnert,
Alejandra Martínez-Monés &
Christophe Reffay

2. Agenda
13.30 Introduction of presenters and participants
13.45 Use Cases of SNA4LA
14.15 SNA Basics
15.15 Description of the practical workbench
15.30 COFFEE BREAK
16.00 Hands-on experiences using the workbench
17.30 End of the tutorial
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3. Introduction of presenters
& participants
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4. Tutorial Presenters
• Andreas Harrer
• Tillman Göhnert
• Alejandra Martínez-Monés
• Christophe Reffay
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5. Use Cases of SNA for LA
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6. Identifying Participatory
Roles in CSCL scenarios
Marcos García, J.A., Martínez Monés, A., Dimitriadis, Y., Anguita
Martínez, R. A role-based approach for the support of
collaborative learning activities e-Service Journal. 6(1):40-58,
Diciembre 2007
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7. Participatory roles
• Goal: Identifying roles based on their
position within a network of relationships
o Description of expected roles, based on centrality indexes
o Identify the emergence of those roles in an experience
o Provide them with information adapted to their needs
• Approach:
o Description of roles based on “fuzzy” combinatios of SNA
indexes
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8. Participatory roles
(Distance forum, CSCL 2009)
Isolated
Non-participative

9. Role: Dynamizer student
Indicators
Outdegree CDo(i)
Description Number of links initiated by this actor.
V a l u e s /
Interpretation
A high value, indicates a high participation of
the actor
R e l e v a n c e
rank
First
Outdegree sessions
Description Specifies the relation between participation
and number of sessions
V a l u e s /
Interpretation
A high value indicates a high participation of
the actor in the overall activity
R e l e v a n c e
rank
Second
Indegree CDi(i)
Description Number of links terminating by this actor
V a l u e s /
Interpretation
A medium value indicates a medium
relevance
R e l e v a n c e
rank
Third.
Participatory roles
Dynamizer student
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10. Student dynamizer
(Distance forum, CSCL 2009)
Animator
CDo(B20) = 16
CDo-sessions (B20) = 30,8%
CDi (B20) = 4 (17th value)
solated
Non-participative

11. Student dynamizer
(Web-­‐‑based document sharing)
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aalobel
abalarr
aordboa
arodull
cfergon
cgeiveg
cgonzrol
cjimcab
emonveg
emunsei epadgon
esasbaz
estibaliz
ggj
ibalala
ilizmar
imunado
jjimrio
jorge
lcaravi
lconase
lhergar
Lmunbla
marnmar
Mcamalo
mferrub
mlauroth
mmaygom
mmiggut
ncalgua
Noelia
papajim
plagvel
ppersan
profe
rapaduq
rfueote
rgorvil
rmarcol
rpermar
Rumbram
scilram
scunfer
sfermar
smarmor
vdiefer
Vmaybar
Animator

12. Cohesion in subgroups
Reffay, C. and Chanier, T., (2003) How social network analysis
can help to measure cohesion in collaborative distance
learning, Proc of CSCL, 2003
Reffay, C., Teplovs, C., & Blondel, F.-M. (2011). Productive re-
use of CSCL data and analytic tools to provide a new
perspective on group cohesion. Proc of CSCL 2011.
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13. Cohesion
• Simugline data set
o 4 online groups working on an French as foreign language simulation
o Each group had an instructor and a
• Data
o Discussion forums that are local to each of the 4 groups
• Network
o The relation between “a” and “b” represents messages sent by “a” and
opened by “b” plus messages posted by “b” and opened by “a”
• Indexes
o Cliques at level “c”: subgroup in which the ties between all pairs of agents
have values c or greater (i.e., have exchanged c or more messages).
o “c” can be a value announced by the teacher as the desirable level of
interaction
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14. Comparing groups with (level
10) cliques
Aquitania
Gallia
Lugdunensis
Gallia
Narbonensis
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15. Hierarchical Clusters
GALLIA G
G G G G G G G G G l
l l n l G l n l l l 1
Level 3 2 1 1 t 4 2 6 5 9 0
----- - - - - - - - - - - -
167 . . . XXX . . . . . .
108 . . . XXXXX . . . . .
83 . . XXXXXXX . . . . .
64 . . XXXXXXXXX . . . .
52 . XXXXXXXXXXX . . . .
42 XXXXXXXXXXXXX . . . .
29 XXXXXXXXXXXXXXX . . .
9 XXXXXXXXXXXXXXX XXXXX
5 XXXXXXXXXXXXXXXXXXXXX
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16. PaNern (Star) => Intensity?
Aquitania
Lugdunensis Narbonensis
GalliaIntensity=192
Intensity=12 Intensity=72
Intensity=111
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17. The “fourth” man
Malzahn, N., Harrer, A., & Zeini, S. (2007). The Fourth Man -
Supporting self-organizing group formation in learning
communities. In Proc. of CSCL 2007 (pp. 547–550).
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18. 18
Person-­‐‑
Topic-­‐‑
Network
from Forum:
group
searches for
the „fourth
man“
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19. 19
Network
using
semantic
relations
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20. Blockmodeling
Harrer, A. & Schmidt, A. (to appear 2013). Blockmodeling and
role analysis in multi-relational networks. Social Networks and
Mining. Springer. 2013
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21. 21
Complex networks – dissolving the Death Star
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22. 22
Complex networks – dissolving the Death Star
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23. 23
Complex networks – dissolving the Death Star
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24. A Blockmodel of this network –
positions and reduced matrix
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25. SNA basics
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26. SNA basics
• What is a Social Network?
• Types of networks and network transformations
• Useful definitions and measures on graphs
• Grouping concepts
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27. What is a social network?
• A set of nodes (actors)
o Persons
o Groups
o Organizations
o Objects
o …
• A set of relationships
o Is a friend of
o Is neighbour of
o Provides goods to …
o Has sent a message to …
o Etc.
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28. What is a social network?
• Complexity may
increase.
• Analysis cannot
be done by
hand
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29. Ego-­‐‑net : The network of
ego
• Ego: the selected node
• Alters (neighbours): distance (Ego,Alter) ≤ 1
o Ties between ego and alter
o Ties between alters
Whole network Ego-net (x34)Ego-net (x38)
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30. Types of Social Networks
According to…
• Number of sets of actors
o One-mode : one set of actors
o Two-mode : (Bi-partite, affiliation networks) two sets of actors
• Relationships
o Directed or undirected
o Valued or un-valued (1/0)
• How are they built
o Complete networks
o Ego-networks
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31. One-­‐‑mode or two-­‐‑mode
networks
All nodes are of the same type
• Administrators
• Societies
Two-modeOne-mode
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Nodes belong to two
sets
• Students

32. Directed vs Undirected
graphs
• Directed
Undirected
Edges are oriented
Edges are not oriented
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33. Weighted (valued) vs
Unvalued graphs
• Weighted/Valued • Unvalued
Edges have values
Edges have no value
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34. Conclusion: 8 possible
network types
One-mode
(One node type)
Two-mode
(Two node types)
• One-Mode
• Directed
• Valued
• One-Mode
• Undirected
• Valued
• One-Mode
• Directed
• Unvalued
• One-Mode
• Undirected
• Unvalued
• Two-Mode
• Directed
• Valued
• Two-Mode
• Undirected
• Valued
• Two-Mode
• Directed
• Unvalued
• Two-Mode
• Undirected
• Unvalued
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35. Network types
transformation allowed
Two-­‐‑Mode
One-­‐‑Mode
Directed
Undirected
Valued
Unvalued
More information
Less Information
Selection strategy
Not reversible!
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36. Two-­‐‑Mode
One-­‐‑Mode
2
2
1
1
1
1
Do blue nodes share any orange resource? => Unvalued
How many orange resource do blue nodes share ? => Valued
Strategy: Decide what sharing resource represent for relationships between (blue) nodes.
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37. Directed
Undirected
Are nodes connected (one tie is enough)?
Are nodes connected with reciprocal edges?
Strategy: Decide if you have/not edges in both directions.
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38. Valued
Unvalued
Threshold=5
Strategy: Only ties with value>=Threshold are considered
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39. Useful measures of social
networks
• Density
• Degree, In-degree, Out-degree
• Path, Geodesic distance, Diameter
• Centrality indexes (for nodes)
o Degree centrality
o Betweenness centrality,
o Closeness centrality
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40. Eff.=0
Poss.=10
d=0
Eff.=2
Poss.=10
d=0.2
Eff.=4
Poss.=10
d=0.4
Eff.=8
Poss.=10
d=0.8
Eff.=10
Poss.=10
d=1
Density (of edges) for an
undirected graph
edgespossiblenb
edgeseffectivenb
ddensity =
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41. Density (of edges) for a
directed graph
Eff.=0
Poss.=20
d=0
Eff.=4
Poss.=20
d=0.2
Eff.=8
Poss.=20
d=0.4
Eff.=16
Poss.=20
d=0.8
Eff.=20
Poss.=20
d=1
Reciprocal edges count twice
(twice more possible edges)
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42. 1 4
2 6
5 8
3 7
Net A
1
2
4 5
6
8
3 7
Net B
The structure as a constraint
Do nodes “4” and “5” have the same role in nets A and B?
Density:
DA=9/28=0,321
Density:
DB=9/28=0,321
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43. Centrality
• Who is central in this
network?
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44. Degree in an undirected
graph
• For a node, Degree = number of edges
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45. In-­‐‑ & Out-­‐‑ degree in an
directed graph
In-­‐‑degree = number of edges coming into the node
Out-­‐‑degree = number of edges coming out of the node
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46. Path : sequence of edges
connecting 2 nodes
A
H
B
C G
D
E F I J
From A->E : 2 possible paths:
• (A B C E)
or
• (A B D E)
Example in a directed graph
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47. Path: example in an
undirected graph
A
H
B
C G
D
E F I J
From A->E : 2 possible paths:
• (D E)
or
• (D B C E)
Geodesic Distance:
Length of the shortest path
d(D,E) = 1
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48. Diameter of the graph
• Diameter = longest distance in the graph
= maximal distance between any pair of nodes
What is the diameter of this graph?
D = 7
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49. Betweenness centrality
• Number of shortest paths passing through the node
Directed graph
Undirected graph
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50. Closeness centrality
Scoring the closeness of one node to all others
Undirected graph
Directed graph
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51. The Moreno’s
experiments (1943)
Pupils relation in the
classroom:
• Pupils of various age
range
• Gender study
« If you could choose freely,
which are the (2) kids you
would like to have as
direct neighbour? »
Main results:
At <age> => pupils tend to <?>
• 6-8 years old => mix
• 8-13 years old => separate
• 13-15 years old => mix
• 15-17 years old => separate
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52. Moreno’s network
• Who is central in this network?
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53. Components
Removing bridges
(cut-­‐‑points)…
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54. …This results in breaking
the component
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55. Grouping concepts – an
overview
Groups can be determined according to different criteria
• Reachability and Distance – group member is
connected via short ways to all other group members
o Direct links – Clique as complete subgraph
o Relaxing the distance – n-Clique requires all nodes being connected via short
path (lesser and equal than n)
• Node degree – group member should be connected to
many group members
o Leaving out a small number of group members: k-Plex
o Having at least k group members as direct neighbours – k-Core
• Contrasting “ingroup” and “outgroup” – density inside is
much higher than outside
o Alliance: only links to ingroup, no links to outside
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56. Grouping concepts – an
overview
• Group concepts fall in two categories:
o Overlapping concepts
• e.g. Cliques
o Disjunct concepts
• e.g. k-cyclic blocks
• Depending on the type of analysis both categories
have their merits
o Disjunct concepts allow clear-cut assignment to one group
o Overlapping concepts allow analysis of transfer ideas, e.g. Clique
percolation
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57. Cliques or K-­‐‑cliques
Clique: maximum subset where all
nodes are connected
K-clique: Clique with K members
How many
cliques?
• One 5-clique
• One 4-clique
• One 3-clique
• Three 2-cliques
=> 6 cliques
Which are… ?
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58. K-­‐‑cores
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Taken from: V. Batagelj, A. Mrvar / Social Networks 22 (2000) 173-186

59. Clique Percolation
Method
• CPM allows overlapping communities
• Idea: a k-clique “percolates” through the graph
• Overlapping members can be “brokers” between
groups
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Taken from: Wikipedia

60. Visualization influences
Interpretation
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61. Practical Workbench
Presentation
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62. Task one: Simuligne
Data Preprocessing
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Raw Data: A network
with weighted, directed
edges
(Number of forum posts
opened)
Preprocessing:
Symmetrisation of edge
weights
(by minimum, maximum,
sum, or average)

63. Task one: Simuligne
• Choose the data set based on preprocessing
o Narbo_Max: Maximum of both directions
o Narbo_Mean: Average of both directions
o Narbo_Min: Minimum of both directions
o Narbo_Sum: Sum of both directions
• Think of the format transformation (UCINET -> SISOB)
• Focus on the appropriate intensity level of the
relation
• Identify groups
• Choose an appropriate output representation
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64. Task two: Collaboration
over Artifacts (BSCW)
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• Two node types
o BSCW (document sharing) folders as artifacts (-..)
• One folder for general information
• Folders for individual case studies
o Pairs of students, each working mainly on a single case (x..)
• Edges weighted by access

65. Task two: Collaboration
over Artifacts (BSCW)
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• Choose one of the data sets
o sp1_B_cli_cp_U.txt
o sp2_B_cli_cp_U.txt
o sp3_B_cli_cp_U.txt
o spf_B_cli_cp_U.txt
• Think of the format transformation (UCINET -> SISOB)
• Try to identify the general folder
• Try to identify the projects the pairs of students
worked on
• Analyse the collaboration between the students
(hint: Folding is also in the R-Analysis)