Social Networks
TU/e - 0ZM05/0EM15/0A150
1
Obesity as a
networked concept
TU/e - 0ZM05/0EM15/0A150
2
TU/e - 0ZM05/0EM15/0A150
3
The same goes for smoking …
TU/e - 0ZM05/0EM15/0A150
4
www.tue-tm.org/INAM

All course info,
literature, slides,
and messages can
be found here.
Check regularly!
TU/e - 0ZM05/0EM15/0A150
5
Today

Course design and content

Introduction to network analysis and concepts
TU/e - 0ZM05/0EM15/0A150
6
Lecturers
Chris Snijders
[email protected]
Uwe Matzat
[email protected]
Rudi Bekkers
[email protected]
Mila Davids
[email protected]
Gerrit Rooks
[email protected]
TU/e - 0ZM05/0EM15/0A150
7
The course: organization



Three courses:
0ZM05
(5 ects)
0EM15
(6 ects)
0A150
(3 ects)
Lectures every week on Wednesdays, hours 7 and 8. Later in
the program less lecture time, more "assignment time" (see
the course website).
Different courses, so not everybody has to do the same ...
TU/e - 0ZM05/0EM15/0A150
8
Rough outline for the different courses
(see online for the details)
Topic
0em15
0zm05
0a150
Basic stuff (about 5 lectures)
Yes
Yes
Yes
Assignment CS
Yes
Yes
Yes
Assignment UM
Yes
Yes
No
Personal and business
networks + assignment GR
No
No
Yes
Dynamic capabilities and
knowledge transfer in
networks
Yes
No
No
Exam
Yes
Yes
No
(have to be
there)
+ survey completion (so that you experience what a network survey
feels like, and we can analyze the data during class and assignments)
TU/e - 0ZM05/0EM15/0A150
9
TU/e - 0ZM05/0EM15/0A150
10
Course requirements

0em15/0zm05:
Two (group of 2) assignments + written exam.
Grade = 50% assignments + 50% exam.
Both assignments and the exam should be at least
a 4.0. Final grade should be at least 5.5.
For 0a150 it’s the average of the two
assignments, where both should be at least 4.0
and the average at least 5.5
TU/e - 0ZM05/0EM15/0A150
11
To do: register in Studyweb
(if possible)
TU/e - 0ZM05/0EM15/0A150
12
TU/e - 0ZM05/0EM15/0A150
13
Course aim
knowledge about concepts in
network theory, and being able to
apply that knowledge
(with an emphasis on innovation and
alliances)
TU/e - 0ZM05/0EM15/0A150
14
The setup in some more detail
Network theory and background
-
-
Introduction: what are they, why important …
Four basic network arguments
Kinds of network data (collection)
Typical network concepts
Visualization and analysis
TU/e - 0ZM05/0EM15/0A150
15
Some historical background
and a general intro
TU/e - 0ZM05/0EM15/0A150
16
It’s about making our 'social space' visible
"If we ever get to the point of charting a
whole city or a whole nation, we would have
… a picture of a vast solar system of
intangible structures, powerfully influencing
conduct, as gravitation does in space.
Such an invisible structure underlies society
and has its influence in determining the
conduct of society as a whole."
Jacob L. Moreno
New York Times, April 13, 1933
TU/e - 0ZM05/0EM15/0A150
17
We live in a connected world
“To speak of social life is to speak of the
association between people – their
associating in work and in play, in love and
in war, to trade or to worship, to help or to
hinder. It is in the social relations men
establish that their interests find
expression and their desires become
realized.”
Peter M. Blau
Exchange and Power in Social
Life, 1964
TU/e - 0ZM05/0EM15/0A150
18
Why do networks matter?
TU/e - 0ZM05/0EM15/0A150
19
Why do networks matter?
TU/e - 0ZM05/0EM15/0A150
20
Social Networks –
a (cheesy) introduction
http://www.youtube.com/w
atch?v=6a_KF7TYKVc
TU/e - 0ZM05/0EM15/0A150
21
Social network analysis – it's core
An interdisciplinary perspective emphasizing structural
relationships as key explanatory concepts and principles:
• Structural properties of social formations are contexts
that shape the perceptions, beliefs, attitudes, and
actions of individuals and collectivities
• Social influence and collective action may be facilitated
and/or constrained by direct and indirect exchanges
(transactions) among social actors possessing
differential resources (e.g., information)
• Actors and transactions/interactions between actors
are embedded, i.e. located within actual situational
contexts
TU/e - 0ZM05/0EM15/0A150
22
The network perspective
Two firms in the same market.
Which firm performs better (say, is more innovative): A or B?
A
B
This depends on:
•Cost effectiveness
•Organizational structure
•Corporate culture
•Flexibility
•Supply chain management
•…
TU/e - 0ZM05/0EM15/0A150
23
The network perspective
Two firms in the same market.
Which firm performs better (say, is more innovative): A or B?
A
B
Note
and ... on the structure of the network
TU/e - 0ZM05/0EM15/0A150
Networks are
one way of
dealing with
“market
imperfection”
24
Multi-level and interdisciplinary
Network applications appear in diverse substantive fields of
mostly social sciences – anthropology, management, political
science, public health, sociology (and recently also in
economics)
Studies span micro- meso- & macro-levels of analysis:
• personal social & health support systems
• children’s play groups, high school cliques
• employee performance
• neighboring behavior, community participation
• work teams, voluntary associations, social movements
• military combat platoons, terrorist cells
• corporate strategic alliances, board interlocks
• international relations: trade, aid, war & peace
•Internet relations: Twitter, LinkedIn, Facebook
TU/e - 0ZM05/0EM15/0A150
25
It's a science ...
TU/e - 0ZM05/0EM15/0A150
26
Example: crime research
Example topics
-"Cold case" research
- forensic psychiatry
-(youth) crime
-...
TU/e - 0ZM05/0EM15/0A150
27
Articles with Network* Keyword
5000
4000
3000
SocAbs
2000
EconLit
1000
0
1965-70
1970-75
1975-80
1980-84
1985-89
YEAR
4/13/2015
SOURCES: Sociological Abstracts, EconLit
1990-94
1995-99
2000-04
Network analysis: origins
Started in 1920s, Jacob L. Moreno
pioneered social network analysis for his
“psychodrama” therapy. He used
sociomatrices and hand-drawn sociograms
to display children’s likes and dislikes of
classmates as directed graphs (digraphs).
TU/e - 0ZM05/0EM15/0A150
29
Moreno’s socio-matrix
TU/e - 0ZM05/0EM15/0A150
30
… displayed as a sociogram
TU/e - 0ZM05/0EM15/0A150
31
Example: A targeted approach to HIV prevention
Think about similar examples
for:
• Introduction of new products
into target groups
•…
TU/e - 0ZM05/0EM15/0A150
32
Modern computing makes a big difference
“Visualization has been a key
component of social network
analyses from the beginning,
proliferating into today’s
dazzling computer-based
multidimensional displays”
(Freeman 2001)
TU/e - 0ZM05/0EM15/0A150
33
Social network software
1)
UCINet – Many things on network analysis
Lin Freeman, Steve Borgatti, Martin Everett
2)
MultiNet – Whole Network Analysis
+ Nodal Characteristics
3)
P*Star – Dyadic Analysis – Stan Wasserman
4)
NodeXL (an Excel plugin) – Marc Smith
5)
Pajek – Network Visualization – Supersedes Krackplot
6)
7)
StocNet – Tom Snijders - collected programs for, e.g., analysis of
dynamic networks
… and many others
NB Even though computers
are fast, really large networks
can still be a real problem
TU/e - 0ZM05/0EM15/0A150
34
Definitions and other boring stuff
TU/e - 0ZM05/0EM15/0A150
35
Social network basics

A network (or graph)
contains a set of actors (or
nodes, objects, vertices),
and a mapping of relations
(or ties, or edges,
connections) between the
actors
1
2
For instance:
Actors: persons
Relationships: “participates in the same
course as”
Or:
Actors: organizations
Relationships: have formed an alliance
TU/e - 0ZM05/0EM15/0A150
36
Social network concepts: ties

Relationships can be
directed:
1
2
For instance: person 1 likes person 2

Symmetrical by choice:
1
2
Person 1 likes 2, 2 likes 1

Symmetrical by definition: 1
2
Person 1 is married to 2
(usually depicted as)
TU/e - 0ZM05/0EM15/0A150
1
2
37
Social network concepts: weights

Relationships can carry
weights :
1
2
3
4
Actors: persons
Relationships: know each other
3 and 4 know each other better (stronger
tie)

Actors can have a variety
of properties associated
with them:
TU/e - 0ZM05/0EM15/0A150




38
Social networks: translating arguments
There is reciprocity: whenever there is a tie from a to
b, there also is a tie from b back to a
Actor A is powerful: many connections go through A
1
2
TU/e - 0ZM05/0EM15/0A150
3
39
Quantifying matters through network concepts


Actor characteristics:
 outdegree
 indegree
 betweenness
 ... (and many more)
Network characteristics
 density
 segmentation
 distribution of outdegrees
 ... (and many more)
TU/e - 0ZM05/0EM15/0A150
40
More examples
TU/e - 0ZM05/0EM15/0A150
41
An example of a modern network:
9-11 Hijackers Network
SOURCE: Valdis Krebs
http://www.orgnet.com/
TU/e - 0ZM05/0EM15/0A150
42
OECD Trade Flows 1981-1992
Note: practical
use of
visualization
diminishes as
networks
grow larger
SOURCE: Lothar Krempel
TU/e - 0ZM05/0EM15/0A150
http://www.mpi-fg-koeln.mpg.de/~lk/netvis.html
43
Internet facilitates social networking…
TU/e - 0ZM05/0EM15/0A150
44
… for recreational use …
TU/e - 0ZM05/0EM15/0A150
45
… also for business purposes …
http://www.youtube.com/watch?v=6SSR2tg5n_U
TU/e - 0ZM05/0EM15/0A150
46
… or, if you want to create your own
FaceBook-like site …
http://www.vivalogo.com/vl-resources/opensource-social-networking-software.htm
BTW Lots of
businesses are willing
to do the dirty work for
you …
TU/e - 0ZM05/0EM15/0A150
47
Organizations as networks:
org-chart shows formal ties…
SOURCE: Brandes, Raab and Wagner (2001)
<http://www.inf.uni-konstanz.de/~brandes/publications/brw-envsd-01.pdf>
TU/e - 0ZM05/0EM15/0A150
48
… but the graph of actual connections
is really different
TU/e - 0ZM05/0EM15/0A150
49
… and can be restructured to reveal the “real”
hierarchy!
TU/e - 0ZM05/0EM15/0A150
50
Networks and innovation
TU/e - 0ZM05/0EM15/0A150
51
Why networks & innovation?



Classic innovation studies focus mainly on characteristics of
individuals or firms to explain innovation
 e.g. firm size and innovativeness
However, innovation, is inherently social in nature
 e.g. firms have relations with other firms and
consequently access to additional external resources
Hence, networks of social relations between actors
 (individuals and organizations) may be important factors
in explaining innovation
 and innovation may change networks of social relations
as well
TU/e - 0ZM05/0EM15/0A150
52
Why networks and alliance management?
The knowledge economy is a network economy
Third Industrial Revolution
Second Industrial Revolution
CEO
Staff
Divisions
Guild
Master
Pupil
Master
Pupil
Master
Pupil
Networked model:
Economies of skill:
-access to knowledge
-co-development
-leverage knowledge
-focus on core
competences
-learn and innovate
‘Stand alone’ model:
- Economies of scale
- Optimize assets
Organizational models are transforming from “stand alone” to “networked”
TU/e - 0ZM05/0EM15/0A150
53
CEOs rate alliances among
the most important management tools
Bain researched the 25 most
popular management tools
in a survey among 960
international executives
• Alliances are among the 10 most
widely used tools by top
executives
• 63% of them use alliances
• Note that other tools involve
alliance and network related
aspects as well: CRM, outsourcing,
growth strategies, supply chain
management
Source: Rigby, 2005, Management Tools 2005, Bain & Company
TU/e - 0ZM05/0EM15/0A150
54
Alliances lead to networks
Network in Flat Screens 2000-2001
Source: De Man, 2006,
Alliantiebesturing
In 2 years time 75% of the firms in the industry are directly or indirectly connected
TU/e - 0ZM05/0EM15/0A150
55
Network questions and arguments
TU/e - 0ZM05/0EM15/0A150
57
Typicalities of network arguments


Non-linear effects can occur easily (cf “Smallworld phenomenon”) in networks [lecture 3]
Data collection often daunting
= “is being eaten by”
TU/e - 0ZM05/0EM15/0A150
58
TU/e - 0ZM05/0EM15/0A150
59
Typical network related questions



Which of these actors has the best position in the
network?
 Example: firms in alliance networks
Which kinds of networks are best for <…>
purposes?
 Example: R&D teams
Which are the key relations in the network?
 Example: terrorism
TU/e - 0ZM05/0EM15/0A150
60
Networks = Y
or
Networks = X
In most social science applications, networks are considered
as an independent variable.
For instance
Firm A performs better than B because firm A is embedded
in a network with a lot of ties (a network of higher “density”)
or
Person A performs better than B because person A has a lot
of ties to other persons and person B doesn’t
(firm A has a higher “outdegree”)
TU/e - 0ZM05/0EM15/0A150
61
Networks = Y
or
Networks = X
Sometimes: networks as the dependent variable
For instance:
How do the social networks of successful people differ from
the social networks of others? (and why is that?)
And, even rarer: dynamic network theory
For instance:
How do the friendship networks of people change over time?
TU/e - 0ZM05/0EM15/0A150
62
Using network arguments...



Make sure that you define the actors/nodes, and
what the ties between them represent (directed?,
weighted?).
Make clear how and what (kind of) network
characteristics drive your result. There are so
many network characteristics … think hard!
Shop around for arguments in areas unrelated to
your own! (where perhaps only the nodes and the
ties are different!)
“The best ideas already exist”
TU/e - 0ZM05/0EM15/0A150
63
Kinds of network arguments
(in detail next week)




Closure competitive advantage stems from managing risk; closed
networks enhance communication and enforcement of sanctions
Brokerage competitive advantage stems from managing
information access and control; networks that span structural holes
provide the better opportunities
Contagion information is not a clear guide to behavior, so
observable behavior of others is taken as a signal of proper
behavior.
[1] contagion by cohesion: you imitate the behavior of those
you are connected to
[2] contagion by equivalence: you imitate the behavior of those
others who are in a structurally equivalent position
Prominence information is not a clear guide to behavior, so the
prominence of an individual or group is taken as a signal of quality
TU/e - 0ZM05/0EM15/0A150
64
To Do:

follow the directions on
www.tue-tm.org/INAM

Studyweb: register!
TU/e - 0ZM05/0EM15/0A150
65
Download

slides - Chrissnijders