Ringvorlesung der Research School Business & Economics (RSBE) University of Siegen, Germany June 28, 2011 Ralf Klamma RWTH Aachen University

1. Informatik 5 (DBIS)
RWTH Aachen University
TeLLNet
GALA
Network Flow and Network Formation:
A Social Network Analysis Perspective
Ralf Klamma
RWTH Aachen University
Ringvorlesung der Research School Business & Economics (RSBE)
Siegen
June 28, 2011
Lehrstuhl Informatik 5
(Informationssysteme)
Prof. Dr. M. Jarke
I5-KL-111010-1

2. Agenda
TeLLNet
GALA
Conclusions and Outlook
Network Formation
Network Science
Network Flow
Lehrstuhl Informatik 5
(Informationssysteme)
Prof. Dr. M. Jarke
I5-KL-111010-2

3. RWTH Aachen University
• 260 institutes in 9 faculties as Europe’s
leading institutions for science and research
TeLLNet • Currently around 31,400 students are enrolled
GALA in over 100 academic programs
• Over 5,000 of them are international students
hailing from 120 different countries
• 1,250 spin-off businesses have created
around 30,000 jobs in the greater Aachen
region over the past 20 years.
• IDEA League
• Germany’s Excellence Initiative:
3 clusters of excellence, a graduate school
Lehrstuhl Informatik 5 and the institutional strategy “RWTH
Aachen 2020: Meeting Global Challenges”
(Informationssysteme)
Prof. Dr. M. Jarke
I5-KL-111010-3

4. Community Information Systems
Research Group
TeLLNet
GALA
Established at DBIS chair, RWTH Aachen University
Lehrstuhl Informatik 5
3 Postdocs, 7 PhD students,
(Informationssysteme)
Prof. Dr. M. Jarke + paid student workers & thesis workers
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5. TeLLNet
GALA
Network Science
Lehrstuhl Informatik 5
(Informationssysteme)
Prof. Dr. M. Jarke
I5-KL-111010-5

6. Questions within Network Science
 How well the position of a agent is to receive and
TeLLNet
disseminate information?
GALA
– experts (centrality measures) [Wasserman & Faust,
1997]
 Are users communicate only within their groups
or with some agents from the other groups as well?
– innovation stars (boundary spanners, brokers, high
betwenness centrality) [Burt, 2005]
 Who and what effects a agent?
– influence networks [Lewis, 2008]
 What are groups/communities an agent
belongs to?
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– community mining [Clauset et al., 2004]
(Informationssysteme)
Prof. Dr. M. Jarke
I5-KL-111010-6

7. Executive Board Networks:
TheyRule.net
 A prototype as of 2004
TeLLNet  What is the connection between Motorola and Whirlpool?
GALA
 How does the academic institutes and the companies
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network look like?
(Informationssysteme)
Prof. Dr. M. Jarke
I5-KL-111010-7

8. Who rule 3M, Motorola, AT&T, Coca-
Cola, PepsiCo, and McDonald‘s?
TeLLNet
GALA
Lehrstuhl Informatik 5
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Prof. Dr. M. Jarke
I5-KL-111010-8

9. Spread of Contagion
TeLLNet
GALA
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(Informationssysteme)
Prof. Dr. M. Jarke Source: orgnet.com
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10. Network Science Paradigms
 Merge of analytic and engineering paradigms
 In an analytic discipline
TeLLNet
GALA Scientific
– To find laws (computing paradigms) disciplines Commerce
– To generate phenomena
Communication
– To explain observed phenomena serves a
 In a engineering discipline purpose
– To realize and implement Entertainment Politics
the paradigms of Networks
– To understand the cases when particular technologies should be
used
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(Informationssysteme)
– To store Network data efficiently (Mediabase)
Prof. Dr. M. Jarke
I5-KL-111010-10

11. Web Science:
The Long Tail & Fragments
IN Continent Central Core OUT Continent
TeLLNet
GALA
Tunnels
[Anderson, 2006]
Tendrils Island
[Barabasi, 2002]
 The Web is a scale-free, fragmented network
– The power law (Pareto-Distribution etc.)
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– 95 % of users are located in the Long Tail (Communities)
(Informationssysteme)
Prof. Dr. M. Jarke – Trust and passion based cooperation
I5-KL-111010-11

12. Principle Analytic Approach
 Interdisciplinary multidimensional model of networks
TeLLNet
– Social network analysis (SNA) is defining measures for
social relations
GALA
– Actor network theory (ANT) is connecting human and media agents
– i* framework is defining strategic goals and dependencies
– Theory of media transcriptions is studying cross-media knowledge
social software Media Networks network of artifacts
Wiki, Blog, Podcast, IM, Chat, Microcontent, Blog entry, Message, Burst, Thread,
Email, Newsgroup, Chat … Comment, Conversation, Feedback (Rating)
i*-Dependencies
(Structural, Cross-media)
network of members
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Members
(Social Network Analysis: Centrality,
(Informationssysteme)
Prof. Dr. M. Jarke
Efficiency)
Communities of practice
I5-KL-111010-12

13. MediaBase
 Collection of Social Software
artifacts with parameterized
TeLLNet
PERL scripts
GALA
– Mailing lists
– Newsletter
– Web sites
– RSS Feeds
– Blogs
 Database support by IBM DB2,
eXist, Oracle, ...
 Web Interface based on Firefox
Plugin, Plone/Zope, Widgets, ...
 Strategies of visualization
– Tree maps
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(Informationssysteme)
Prof. Dr. M. Jarke
– Cross-media graphs
I5-KL-111010-13 Klamma et al.: Pattern-Based Cross Media Social Network Analysis for Technology Enhanced Learning in Europe, EC-TEL 2006

14. TeLLNet
GALA
Network Flow
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(Informationssysteme)
Prof. Dr. M. Jarke
I5-KL-111010-14

15. Fundamentals:
Definitions of Network
A network Γ= (N, L) where
Fundamentals
of networks 
TeLLNet N = {1, 2, ..., n} is a (finite) set of nodes (vertices),
GALA
L ⊆ N x N is a set of links (edges)
 Assumed:
– Unweighted
– No multiple links
=> only one link exist between two given nodes
=> these two nodes are neighbors or adjacent
– Directed or undirected
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Prof. Dr. M. Jarke
I5-KL-111010-15

16. Definitions in a Network
Fundamentals
of networks  Degree of a node: z = { j ∈ N : ij ∈ L}
i
TeLLNet
GALA number of incoming and outgoing links
 A path is a sequence of nodes v0, …, vn-1
with (vi, vi+1) ∈ L, for 0 ≤ i < n-1,
A path is a set of connected links
 Length of a path : number of links on a path
 A path is a simple path, if all vertices on a path are pair wise
different
 A cycle is a path with v0 = vn-1 and length n ≥ 2
 A subnetwork of a network Γ= (N, L) is a graph Γ’= (N’,
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(Informationssysteme)
L’) with N’ ⊆ N und L’ ⊆ L
Prof. Dr. M. Jarke
I5-KL-111010-16

17. Representation of Networks
Fundamentals
of networks  Adjacency matrix representation
TeLLNet An n x n-dimensional matrix A, where
GALA
1 if (i, j)∈ L
aij =
0 otherwise
 Neighborhood N ≡ { j ∈ N : (i , j ) ∈ L}
i
 Any network is the collection of neighborhoods
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Γ= N { } i
i∈Ν
Prof. Dr. M. Jarke
I5-KL-111010-17

18. Boolean Adjacency Matrix Example
Fundamentals
of networks  For Network Γ1, the adjacency matrix is as follows:
TeLLNet true =1, if there exists a link between two nodes
GALA
false = 0, otherwise
0 1 2
Incoming degree
0 1 2 3 4
0 0 1 0 1 0
Outgoing degree
1 1 0 0 1 0
3 4
2 0 0 1 0 1
3 0 0 0 0 1
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4 0 0 1 0 0
Prof. Dr. M. Jarke
I5-KL-111010-18

19. Important Types of
Degree Distribution
Fundamentals
of networks
 For any network Γ, its (kth-order) degree distribution
p(·) specifies 1 for each k = 0,
TeLLNet p(k ) = {i ∈ N : zi = k}
GALA n 1, …, n-1
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Prof. Dr. M. Jarke
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20. Network Characteristics:
Geodesic Distances
Fundamentals
of networks  The average geodesic distance d(i, j) is defined as the
TeLLNet minimum number of links that connect i and j
GALA
if no such path exists, d(i, j)=+∞
 The distribution ϖ specifying the fraction ϖ (r) of nodes pairs
at distance r {(i, j) ∈ N × N : d (i, j) = r}
ϖ (r) =
n(n − 1)
where ∑r >0ϖ (r) = 1
 The average network distance d = ∑ rϖ (r)
0< r <∞
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 The diameter of the network d = max{r : ϖ (r) > 0}
ˆ
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21. Network Characteristics:
Density
TeLLNet
GALA
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22. Network Characteristics:
Closeness & Clustering
Fundamentals
of networks  The total distance ∑ j∈N d (i, j)
TeLLNet  The closeness is defined as: c(i) ≡ 1
GALA ∑ j∈N d (i, j )
 For each node i having at least two neighbors: clustering
{ jk ∈ L : ij ∈ L ∧ ik ∈ L}
C ≡
i
zi ( zi − 1)
2
 For each node j having less than two neighbors Cj =0
1 n i
 Clustering index of the network Γ C = ∑C
n i =1
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Prof. Dr. M. Jarke
I5-KL-111010-22

23. Network Characteristics:
Cohesiveness & Betweeness
Fundamentals
of networks  Given a network Γ= (N, L), let M⊂N, for each node i ∈ M
TeLLNet the fraction of its connections i {ij ∈ L : j ∈ M }
GALA
H (M ) =
zi
 The overall cohesiveness of the set M is defined as
H ( M ) = min H i ( M )
i∈M
 if the network Γ is connected the shortest-paths v(j, k) for
each j, k and j≠k the betweenness of node i is
i
v ( j, k )
b ≡∑
i
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j ≠k v( j, k )
Prof. Dr. M. Jarke
I5-KL-111010-23

24. Shortest-path Betweenness:
Example
i
v ( j, k )
Shortest-path betweenness b ≡∑
Fundamentals
of networks  i
TeLLNet  Nodes A and B will have j ≠k v( j, k )
GALA
high (shortest-path)
betweenness in this
configuration, while
node C will not
A measure of the extent to which an actor has control over
information flowing between others
In a network in which flow is entirely or at least mostly along
Lehrstuhl Informatik 5 geodesic paths, the betweenness of a node measures how
much flow will pass through that particular node
(Informationssysteme)
Prof. Dr. M. Jarke
I5-KL-111010-24

25. Flow Betweenness
Fundamentals
of networks  Flow betweenness of a node i is defined as the amount of
TeLLNet flow through node i when the maximum flow is transmitted
GALA from s to t, averaged over all s and t:
f st (i)
i
bmf ≡ ∑s,t∈N ,i ≠ s,i ≠t, f >0
st f st
 While calculating flow betweenness, vertices A and B will
get high scores while vertex C will not
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(Informationssysteme)
Prof. Dr. M. Jarke
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26. Case: AERCS - Recommendation of
Venues for Young Computer Scientists
 DBLP (http://www.informatik.uni-
trier.de/~ley/db/)
TeLLNet
- 788,259 author’s names
GALA - 1,226,412 publications
- 3,490 venues (conferences,
workshops, journals)
 CiteSeerX (http://citeseerx.ist.psu.edu/)
- 7,385,652 publications
- 22,735,240 citations
- Over 4 million author’s names
 Combination
- Canopy clustering [McCallum 2000]
- Result: 864,097 matched pairs
- On average: venues cite 2306 and
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are cited 2037 times
(Informationssysteme)
Prof. Dr. M. Jarke Pham, Klamma, Jarke: Development of Computer Science Disciplines – A Social Network
I5-KL-111010-26 Analysis Approach, SNAM, 2011

27. Properties of Collaboration and
Citation Graphs of Venues
TeLLNet
GALA
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Prof. Dr. M. Jarke
I5-KL-111010-27

28. User-based CF:
Author Clustering
 Data: DBLP
TeLLNet
 Perform 2 test cases for the years of 2005
GALA
and 2006
- Clustering of co-authorship networks
- Prediction of the venue
 Clustering algorithm
- Density-based algorithm [Clauset 2004]
- Obtained modularity: 0.829 and 0.82
 Cluster size distribution follows Power law
Lehrstuhl Informatik 5
(Informationssysteme)
Prof. Dr. M. Jarke
I5-KL-111010-28

29. User-based CF:
Precision and Recall
 Precisions for 1000 random chosen
TeLLNet
authors
GALA  Precisions computed at 11 standard
recall levels 0%, 10%,….,100%
 Results
- Clustering performs better
- Not significant improved
- Better efficiency
 Further improvement
- Different networks: citation
- Overlapping clustering
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(Informationssysteme)
Prof. Dr. M. Jarke
I5-KL-111010-29

30. Item-based CF:
Venue Network Creation and Clustering
 Knowledge network
- Aggregate bibliography coupling counts at venue level
TeLLNet
- Undirected graph G(V, E), where V: venues, E: edges weighted by cosine
GALA
similarity
∑k =1 Bi ,k B j ,k
n
Bi • B j
Ci , j = =
Bi × B j ∑k =1 ,
Bi2k ∑k =1 B 2, k
n n
2 j
2
- Threshold: Ci , j >= 0.1
- Clustering: density-based algorithm [Neuman 2004, Clauset 2004]
- Network visualization: force-directed paradigm [Fruchterman 1991]
 Knowledge flow network
- Aggregate bibliography coupling counts at venue level
- Threshold: citation counts >= 50
 Domains from Microsoft Academic Search
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(Informationssysteme)
Prof. Dr. M. Jarke
(http://academic.research.microsoft.com/)
I5-KL-111010-30

31. Knowledge Network:
the Visualization
TeLLNet
GALA
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32. Interdisciplinary Venues:
Top Betweenness Centrality
TeLLNet
GALA
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Prof. Dr. M. Jarke
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33. High Prestige Series:
Top PageRank
TeLLNet
GALA
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(Informationssysteme)
Prof. Dr. M. Jarke
I5-KL-111010-33

34. TeLLNet
GALA
Network Formation
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(Informationssysteme)
Prof. Dr. M. Jarke
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35. Case: TeLLNet - SNA for European
Teachers‘ Life Long Learning
 How to manage and handle large scale
data on social networks?
TeLLNet
 How to analyse social network data in
GALA order to develop teachers’
competence, e.g. to facilitate a better
project collaboration?
 How to make the network visualization
useful for teachers’ lifelong learning?
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I5-KL-111010-35

36. Analysis and Visualization of
Lifelong Learner Data
 Performance Data on Projects  Network Structures and Patterns
TeLLNet
GALA
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37. Network Formation Strategies
 Homophily – love of the same [LaMe54, MSK01]
TeLLNet
– similar socio-economical status
GALA
– thinking in a similar way
 Contagion
– being influenced by others
 How to represent strategies for lifelong learner?
Lehrstuhl Informatik 5
(Informationssysteme)
Prof. Dr. M. Jarke
I5-KL-111010-37

38. Game Theory Basics
 Every situation as a game [Borel38, NeMo44]
 A player – makes decisions in a game
TeLLNet
GALA
 Players choose best strategies based on payoff
functions
 Payoffs  motivations of players
 A strategy defines a set of moves or actions a player
will follow in a given game (mixed strategy, pure
strategy)
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I5-KL-111010-38

39. Game Theory
A game is a tuple
TeLLNet
GALA
G = N , ( Ai )i∈N , (ui )i∈N , where
N is a nonempty, finite set of players
Each player i∈N has
1. a set of actions (strategy space) Ai
2. payoff functions ui : A  → R
3. payoff matrix
Player B chooses white Player B chooses black
Player A chooses white 1,1 1,0
Lehrstuhl Informatik 5 Player A chooses black 0,1 0,0
(Informationssysteme)
Prof. Dr. M. Jarke
I5-KL-111010-39

40. Network Formation Games
 Social networks are formed by individual decisions
TeLLNet
– Cost: write an e-mail
GALA
– Utility: cooperate with others
 Social networks between pupils
– Cost: make a joke
– Utility: get appreciation from others
 Lifelong learner networks
– Cost: take a learning course
– Utility: find learners with
similar way of reasoning
Lehrstuhl Informatik 5
(Informationssysteme)
Prof. Dr. M. Jarke
I5-KL-111010-40

41. Network Formation
 Set of agents N = {1..., n} which are actors of a
TeLLNet network. i and j are typical members of a set
GALA
 A strategy of an agent i ∈ N is a vector
ai = ( ai ,1 ,..., ai ,i −1 , ai ,i +1 , ai ,n )
where ai , j ∈ {0,1} for each j ∈ N {i}
Actor i and j are connected if ai , j = 1
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Prof. Dr. M. Jarke
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42. Nash Network : Win-Win Situation
 Every agent changes its strategy until all agents are satisfied
TeLLNet with their strategies and will not benefit if they change
GALA strategies (the network is stable)  Nash equilibrium
 A network is a Nash network if each agent is in Nash
equilibrium
 Chosen strategies defeat others for the good of all players
[Nash51, FuTi91]
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Prof. Dr. M. Jarke
I5-KL-111010-42

43. Epistemic Frame for TeLLNet
Identity
• the way how members of a community see themselves in the community
TeLLNet •  institution role, country
GALA Skills
• tasks, community members perform
•  languages, subjects, and tools from projects
Knowledge
• the understanding shared by members of a community
•  languages, subjects
Values
• beliefs of members
• experiences from projects (partners)
Epistemology
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• warrants that justify members’ actions as legitimate
(Informationssysteme)
Prof. Dr. M. Jarke
•  quality labels, prizes, European quality labels
I5-KL-111010-43

44. Multi-Agent Simulation System
 A multi-agent system is a collection of heterogeneous
TeLLNet and diverse intelligent agents that interact with each
GALA
other and their environment [SiAi08]
– Recommendations
Yenta [Foner97] – looking for users with similar interests
based on data from Web media
– Market-binding mechanisms
Looking for the best item (a reward agent, set of items and
users agents) [WMJe05]
– Team formation
Lehrstuhl Informatik 5 Forming teams for performing a task in dynamic
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Prof. Dr. M. Jarke
I5-KL-111010-44 environment [GaJa05]

45. Multi-Agent Simulation Questions
 Which kind of behavior can be expected under arbitrarily
TeLLNet given parameter combinations and initial conditions?
GALA  Which kind of behavior will a given target system display
in the future?
 Which state will the target system reach in the future?
[Troitzsch2000]
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Prof. Dr. M. Jarke
I5-KL-111010-45 2008 2009 2010

46. Agent Based Simulation
 Heterogeneous, autonomous and pro-active actors,
TeLLNet such as human-centered systems
GALA
– Agents are capable to act without human intervention
– Agents possess goal-directed behavior
– Each agent has its own incentives and motives
 Suited for modeling organizations: most work is
based on cooperation and communication
[Gazendam, 1993]
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Prof. Dr. M. Jarke
I5-KL-111010-46

47. Inputs for simulation model
 Agent =Teacher
TeLLNet
 Teacher properties:
GALA
– Languages
– Subjects
– Country
– Institution role
– Any Awards? (European Quality Label or Prize)
 Project properties:
– Languages
– Tools
– Subjects
– Number of pupils in a project
– Age of pupils in a project
– Any Award? (Quality Label)
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(Informationssysteme)
Prof. Dr. M. Jarke
I5-KL-111010-47

48. Network Formation Game Simulation
 Payoff definition: payoff matrix is calculated
TeLLNet dynamically based on Epistemic Frame vector:
GALA
– teachers‘ subjects, subjects of projects (experiences)
– teachers‘ languages, languages of projects (experiences)
– tools used in projects (experiences)
– countries past collaborators are coming from (beliefs)
– ...
 Strategy definition: homophily or contagiosity
 Looking for a suitable network for a teacher and not
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Prof. Dr. M. Jarke
for a suitable partner!
I5-KL-111010-48

49. Nash Equilibrium for
Network Formation
 Finding a Nash Equilibrium (NE) is NP-hard
 Computer scientists deal with finding appropriate
TeLLNet
GALA
techniques for calculating NE with a lot of agents
 We are not interested
in the best solution
but in a better solution
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Prof. Dr. M. Jarke
I5-KL-111010-49

50. Conclusions & Outlook
 Network Science is an interdisciplinary approach between computer
TeLLNet
science and other disciplines
GALA  Mediabase framework based on modeling & reflection support
 Two case studies
– Network Flow: Analysis and visualization of large digital libraries
Identification of basic flow parameters
– Network Formation: Analysis and visualization of large learner networks
Performance Indicators and Visual Analytics
 Application of tools on entrepreneurial problems:
Causation and Effectuation (Excellence Project OBIP at RWTH Aachen
University)
 Researching Network Dynamics by Time Series Analysis and Multi
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Agent Simulation
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Prof. Dr. M. Jarke
I5-KL-111010-50