Network Flow and Network Formation: A Social Network Analysis Perspective

Lehrstuhl Informatik 5(Informationssysteme)

Prof. Dr. M. JarkeI5-KL-111010-1

TeLLNet

GALA Network Flow and Network Formation:A Social Network Analysis Perspective

Ralf KlammaRWTH Aachen University

Informatik 5 (DBIS)RWTH Aachen University

Ringvorlesung der Research School Business & Economics (RSBE) Siegen

June 28, 2011

Vorführender

Präsentationsnotizen

Überschrift in Grau ist für uns intern



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Agenda

Netw

ork S

cienc

e

Netw

ork F

low

Netw

ork F

orma

tion

Conc

lusion

s and

Outl

ook



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RWTH Aachen University

• 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 and the institutional strategy “RWTH Aachen 2020: Meeting Global Challenges”

• 260 institutes in 9 faculties as Europe’s leading institutions for science and research

• Currently around 31,400 students are enrolled in over 100 academic programs

• Over 5,000 of them are international students hailing from 120 different countries



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Community Information Systems Research Group

Established at DBIS chair, RWTH Aachen University3 Postdocs, 7 PhD students,

+ paid student workers & thesis workers



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Network Science



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Questions within Network Science How well the position of a agent is to receive and disseminate information?

– experts (centrality measures) [Wasserman & Faust, 1997]

Are users communicate only within their groupsor with some agents from the other groups as well?

– innovation stars (boundary spanners, brokers, highbetwenness centrality) [Burt, 2005]

Who and what effects a agent? – influence networks [Lewis, 2008]

What are groups/communities an agentbelongs to?

– community mining [Clauset et al., 2004]



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Executive Board Networks: TheyRule.net

A prototype as of 2004 What is the connection between Motorola and Whirlpool?

How does the academic institutes and the companies network look like?



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Who rule 3M, Motorola, AT&T, Coca-Cola, PepsiCo, and McDonald‘s?



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Spread of Contagion

Source: orgnet.com



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Network Science Paradigms

Merge of analytic and engineering paradigms In an analytic discipline

– To find laws (computing paradigms)– To generate phenomena– To explain observed phenomena

In a engineering discipline– To realize and implement

the paradigms of Networks– To understand the cases when particular technologies should be

used– To store Network data efficiently (Mediabase)

Communicationserves a purpose

Scientific disciplines Commerce

Entertainment Politics

Vorführender


(source: CACM paper: pp. 63)



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Web Science: The Long Tail & Fragments

The Web is a scale-free, fragmented network– The power law (Pareto-Distribution etc.)– 95 % of users are located in the Long Tail (Communities)– Trust and passion based cooperation

IslandTendrils

IN Continent Central Core OUT Continent

Tunnels

[Barabasi, 2002]

[Anderson, 2006]



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Principle Analytic Approach Interdisciplinary multidimensional model of networks

– Social network analysis (SNA) is defining measures for social relations

– 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 softwareWiki, Blog, Podcast, IM, Chat, Email, Newsgroup, Chat …

i*-Dependencies(Structural, Cross-media)

Members(Social Network Analysis: Centrality,

Efficiency)

network of artifactsMicrocontent, Blog entry, Message, Burst, Thread,

Comment, Conversation, Feedback (Rating)

network of members

Communities of practice

Media Networks

Vorführender


Improvement !



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MediaBase Collection of Social Software

artifacts with parameterized PERL scripts– 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– Cross-media graphs

Klamma et al.: Pattern-Based Cross Media Social Network Analysis for Technology Enhanced Learning in Europe, EC-TEL 2006



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Network Flow



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Fundamentals: Definitions of Network

A network Γ= (N, L) where N = {1, 2, ..., n} is a (finite) set of nodes (vertices), 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

Fundamentals of networks



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Definitions in a Network Degree of a node:

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’,

L’) with N’ ⊆ N und L’ ⊆ L

Fundamentals of networks }:{ LijNjzi ∈∈=



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Representation of Networks Adjacency matrix representation

An n x n-dimensional matrix A, where

{ }LjiNji ∈∈≡ ),(:N

1 if (i, j) Laij =

Neighborhood Any network is the collection of neighborhoods

0 otherwise

{ } Ν∈=Γ iiN

∈




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Boolean Adjacency Matrix Example For Network Γ1, the adjacency matrix is as follows:

true =1, if there exists a link between two nodes false = 0, otherwise

0 1 2 3 40 0 1 0 1 01 1 0 0 1 02 0 0 1 0 13 0 0 0 0 14 0 0 1 0 0

Incoming degreeO

utgoing degree0 1 2

3 4




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Important Types of Degree Distribution

For any network Γ, its (kth-order) degree distribution p(·) specifies for each k = 0,

1, …, n-1}:{1)( kzNin

kp i =∈=




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Network Characteristics:Geodesic Distances

The average geodesic distance d(i, j) is defined as the minimum number of links that connect i and jif no such path exists, d(i, j)=+∞

The distribution specifying the fraction of nodes pairs at distance r

where The average network distance

The diameter of the network

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Network Characteristics:Density



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Network Characteristics:Closeness & Clustering

The total distance The closeness is defined as:

For each node i having at least two neighbors: clustering

For each node j having less than two neighbors

Clustering index of the network Γ

∑ ∈Njjid ),(

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Vorführender


Closeness is a useful measure in solving location problems: the minimum location problem, also called the median problem or service facility location problem Informational implication: spread of information Weak and strong ties [Granovetter, 1973] Strong ties: transitive Weak ties: transitivity is much less common Strategic implications Network closure [Coleman, 1988]



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Network Characteristics:Cohesiveness & Betweeness

Given a network Γ= (N, L), let M⊂N, for each nodethe fraction of its connections

The overall cohesiveness of the set M is defined as

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

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Vorführender


Intuitively, the centrality of a node measures the importance of this node in bridging the (indirect) connection between other nodes Informational implications A central node is crucial for widespread network communication Jeopardized by the malfunctioning of a central node Becoming congested Strategic implications The very importance of a central node/agent may be translated into an exploitation of this position to its advantage vis-á-vis the remaining nodes



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Shortest-path Betweenness: Example

Shortest-path betweenness Nodes A and B will have

high (shortest-path)betweenness in this configuration, while node C will not

∑≠

≡kj

ii

kjvkjvb),(),(

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 geodesic paths, the betweenness of a node measures how much flow will pass through that particular node




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Flow Betweenness Flow betweenness of a node i is defined as the amount of

flow through node i when the maximum flow is transmitted from s to t, averaged over all s and t:

While calculating flow betweenness, vertices A and B will get high scores while vertex C will not

∑ >≠≠∈≡

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Vorführender


Flow betweenness can be thought of as measuring the betweenness of nodes in a network in which a maximal amount of information is continuously pumped between all sources and targets Maximum flow from a given s to all reachable targets t can be calculated in worst-case time O(l2) and hence the flow betweenness for all nodes can be calculated in time O(l2n)



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Case: AERCS - Recommendation of Venues for Young Computer Scientists

DBLP (http://www.informatik.uni-trier.de/~ley/db/)

- 788,259 author’s names- 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

are cited 2037 timesPham, Klamma, Jarke: Development of Computer Science Disciplines – A Social Network Analysis Approach, SNAM, 2011



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Properties of Collaboration and Citation Graphs of Venues

Vorführender


To gain insights into the above questions, we process as follows: for all the venues, we create their collaboration and citation subgraphs Ga and Gc. [..] By observing the histograms, we are able to understand the nature of computer science venues. The normalized histograms of the four metrics are given in Fig. 9. The metrics reveal some important characteristics of computer science venues. Most of the venues are not narrow and focused on one certain topic, but they are indeed interdisciplinary. This is shown by a large number of low density (Fig. 9a) and low clustering coeffcient (Fig. 9b) citation subgraphs. However, venues also tend to develop a main theme which is the main focused and closely related topics as the core of the venues. That is shown by the large number of big largest connected component venues (Fig. 9c). Now we compare the properties of the collaboration subgraphs and the citations subgraphs. In general, the network metrics are quite similar for both collaboration and citation subgraphs. For example, we observe the same trends for density (Fig. 9a), clustering coefficient (Fig. 9b) and maximum betweenness (Fig. 9d). The betweenness of collaboration subgraphs suggests the existence of the gatekeepers - the key members in every venues, but there are only several important ones, shown by a large number of venues which have low betweenness in collaboration subgraphs. We also observe some differences in largest connected components of collaboration and citation subgraphs (Fig. 9c). While there is a large number of citation subgraphs which have big largest connected component, we observe the opposite trend in the collaboration subgraphs. This observation suggests that there are not so many venues which are successful in stimulating authors to collaborate closely on the main topics, though they are working on the same topics.



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User-based CF:Author Clustering

Data: DBLP Perform 2 test cases for the years of 2005

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



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User-based CF:Precision and Recall

Precisions for 1000 random chosen authors

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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Item-based CF:Venue Network Creation and Clustering

Knowledge network- Aggregate bibliography coupling counts at venue level- Undirected graph G(V, E), where V: venues, E: edges weighted by cosine

similarity

- Threshold: - 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 (http://academic.research.microsoft.com/)

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Knowledge Network:the Visualization



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Interdisciplinary Venues:Top Betweenness Centrality



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High Prestige Series:Top PageRank



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Network Formation



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Case: TeLLNet - SNA for European Teachers‘ Life Long Learning

How to manage and handle large scale data on social networks?

How to analyse social network data in 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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Analysis and Visualization ofLifelong Learner Data

Performance Data on Projects Network Structures and Patterns



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Network Formation Strategies

Homophily – love of the same [LaMe54, MSK01]– similar socio-economical status– thinking in a similar way

Contagion– being influenced by others

How to represent strategies for lifelong learner?

Vorführender


Informatiker heiraten Informatiker Fireflies synchorinizing their lightening – show video



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Game Theory Basics

Every situation as a game [Borel38, NeMo44] A player – makes decisions in a game 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)

Vorführender


The reference should be added to game theory Mixed strategy- if probability over strategis exist, where it is defined how often each move can be played Pure strategy is without any probability



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Game Theory

A game is a tuple, where

N is a nonempty, finite set of playersEach player has

1. a set of actions (strategy space) 2. payoff functions3. payoff matrix

NiiNii uANG ∈∈= )(,)(,

Ni∈iA

R→Aui :

Player B chooses white Player B chooses blackPlayer A chooses white 1,1 1,0Player A chooses black 0,1 0,0

Vorführender


Games can be cooperative and non-cooperative - anyway in both it concerns of a benefit of a particular player We are interested not in a particular player but in a network of players



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Social networks are formed by individual decisions– Cost: write an e-mail– 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

Network Formation Games

Vorführender


Cost of forming Potential utility of linking



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Set of agents which are actors of a network. and are typical members of a set

A strategy of an agent is a vector

where for each

Actor and are connected if

Network Formation

}...,1{ nN =

i

i jNi∈

),,,...,( ,1,1,1, niiiiiii aaaaa +−=}1,0{, ∈jia }{\ iNj∈

j 1, =jia



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Nash Network : Win-Win Situation Every agent changes its strategy until all agents are satisfied

with their strategies and will not benefit if they changestrategies (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]

Vorführender


Not only i and my partner benefit – all benefit! each strategy in a Nash equilibrium is a best response to all other strategies in that equilibrium



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Epistemic Frame for TeLLNet

• the way how members of a community see themselves in the community• institution role, country

Identity

• tasks, community members perform• languages, subjects, and tools from projects

Skills

• the understanding shared by members of a community• languages, subjects

Knowledge

• beliefs of members• experiences from projects (partners)

Values

• warrants that justify members’ actions as legitimate• quality labels, prizes, European quality labels

Epistemology

Vorführender


It is clear that identity for each agent is a teacher. But it is too less for us Values are based on the experiences and the only value that we can‘t take directly as an input into simulation model Community: society Identity: I‘m coming originally from Ukraine Skills: as i‘m working in TEL area i got some ideas about pedagogy: cognetivism, behaviourism, constuctivism Knowledge: computer science background Values: Epistemology: i‘m working at RWTH Aachen as a researcher and doing my PhD



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Multi-Agent Simulation System

A multi-agent system is a collection of heterogeneousand diverse intelligent agents that interact with eachother and their environment [SiAi08]– Recommendations

Yenta [Foner97] – looking for users with similar interestsbased on data from Web media

– Market-binding mechanismsLooking for the best item (a reward agent, set of items and users agents) [WMJe05]

– Team formationForming teams for performing a task in dynamicenvironment [GaJa05]

Vorführender


There are two types multi-agent decision systems and muti-agent simulation systems In the first all agents make a joint decision Unlike analytical models, a simulation model is not solved but is run and the changes of system states can be observed at any point in time.



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Multi-Agent Simulation Questions Which kind of behavior can be expected under arbitrarily

given parameter combinations and initial conditions? Which kind of behavior will a given target system display

in the future? Which state will the target system reach in the future?

[Troitzsch2000]

2008 2009 2010

Vorführender


if this theory has been adequately translated into a computer model this would allow you to answer some of the following questions Initial conditions: teachers looking for partners to create projects Given are teachers with set of parameters What happend next – on flip chart! What is the result after a day/a month/ a year? – what are behaviours of teachers/state of the system - of the network We are focusing more on last two questions that deal with predictions, trends. We use agent based simulation technique



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Agent Based Simulation

Heterogeneous, autonomous and pro-active actors, such as human-centered systems– 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]

Vorführender


ABS models allow one to take both last questions into account? Kind of behaviour – yes , what about the state? Each agent’s behaviour is defined by its own set of attribute values which allows to model variation in each individual’s behaviour and the simulation design is decentralised which allows the agents to be pro-active. Show the video with cleaning robot:



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Inputs for simulation model Agent =Teacher Teacher properties:

– 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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Network Formation Game Simulation

Payoff definition: payoff matrix is calculated dynamically based on Epistemic Frame vector:– 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

for a suitable partner!



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Nash Equilibrium forNetwork Formation

Finding a Nash Equilibrium (NE) is NP-hard Computer scientists deal with finding appropriate

techniques for calculating NE with a lot of agents We are not interested

in the best solutionbut in a better solution



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Conclusions & Outlook Network Science is an interdisciplinary approach between computer

science and other disciplines Mediabase framework based on modeling & reflection support Two case studies

– Network Flow: Analysis and visualization of large digital librariesIdentification of basic flow parameters

– Network Formation: Analysis and visualization of large learner networksPerformance 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 Agent Simulation