Network Theory and Network Data
Volker Schneider
•
•
•
Philosophical background
• Data between theory and facts
• Competing Concepts of Networks
• Network analysis – Theory or Method?
The specificty of network data
• Network analysis ~ structural analysis
• Relational data vs. attribute data
• Representation of relational data
• Areas and levels in relational analysis
Four ways of getting data
• Surveys
• Statistics
• Documentary analysis
• Observations
From Facts and Data to Theory
Naive epistemology
Theory
Generalization of Empirical
rules and patterns
More complex and realistic theory
of scientific knowledge production
General, formal, specific
background knowledge,
background theories
Special
Theories and
Models,
Hypotheses
Problematique
Operationalisation
Facts = Reality =
Experience = Data
Data = representational forms of facts
Mario Bunge (1996) Finding
Philosophy in Social Science
Facts = Reality
No observation and inquiry starts from complete ignorance
•
•
We must know something before we
can observe something
We must know something before we
can formulate a problem and
investigate it
Prior Knowledge
Background knowledge,
theories, concepts
Research
Problem
Theories
Concepts
Models
Hypotheses
Indictors
Es gibt … keine
'voraussetzungslose'
Wissenschaft" (Nietzsche)
Reality
Data
Unobservable
Facts
observable
Quelle: Bunge (1967: 456); leicht modifiziert
Network data as representational form of network facts
•
Network data
• Numbers: matrices, tables
• Graphs: lines, arrows, configuration of lines and arrows
• Animation: Movie
Observation, Measurement
•
Network facts (examples)
• Biological: resource dependencies in a cell, predator-prey relations in a an ecology
• Physical: chemical interaction and reaction
• Technical: Power or telecommunication network
• Societal:
• Social: trust between persons, network of support between community
organizations; migration flows between countries, etc
• Economical: control of capital among corporations, trade between national
economies.
• Political: Subordination among persons, military interventions among countries
Network ≠ network! At least two competing network concepts
•
•
Network1: Most abstract concept; configuration unspecific
→ A certain number of relations connecting social entities.
• Entities may be individuals, groups, organizations; words,
ideas, concepts; production units, resources, etc.
• Relations (or links) may be communication, resources
exchange and control, participation, membership,
complementarity, etc.
Network2: Only special classes or forms of configurations
→ A network is a class of configurations of communications and
transaction links between entities of social transactions which are
neither pure markets nor pure hierarchies.
• Markets are systems of short-term economic transactions
where completely independent actors are coordinated through
“thin” communication links
• Hierarchies are systems with a minimum of transactions and
thick communication links
Examples for network1 - based on graph theory
Multiplex network
B
C
A
Dense all channel
network
B
C
A
F
D
F
B
E
Star network
C
D
E
D
Relation a
Relation b
Relation c
E
Sparse Network
- Chain - A
F
B
C
A
F
D
E
Examples for network2 based on governance theory
Market –
communication
transactions
B
communication &
A
potential
transaction
C
F
Hierarchy
E
Network
B
B
A
C
A
F
F
D
D
E
D
C
E
What is the “social” in social networks?
•
•
•
•
Narrow vs broad view
• Narrow : Non-hierarchical (horizontal) configuration of intense
personal relations that build on trust
• Broad : Any configuration involving social entities and relations.
In a systemic view a society may be subdivided into
• The political system
• The economic system
• The cultural system
In such a perspective, a political network consists of political entities
that are interlinked by political relations:
• Political entities: Actors, resources, institutions, ideologies, etc.
• Relations: Communication, participation, exchange, membership,
etc.
As the “political” may be differentiated into polity, politics and policy,
there may be
• Polity networks
• Networks of politics
• Policy networks
Network analysis and social theory
There is no unified network theory: Many theoretical orientations that are
compatible with “network analysis”:
•
•
•
•
•
•
•
•
•
•
•
•
Structural-functionalism
Interest intermediation
Institutionalism
Governance theory
Resource dependence
Exchange theory
Bargaining theory
Communication theory
Power theory
Rational choice
Structuralist-individualist approach
etc etc
Network metaphers: Net, web, circuit
The Phillips Machine as Iconic, mathematical and practical model for an
economic circuit
Approaches, Metaphors and Images of Policy-Networks
amorph configurations
ordered configurations
contacts
hierarchy
circuit
field of power forces
zwei Netzwerke
Participation, access
governance
structurs
structures of interest
intermediation
ecosystem
netzwork
Ecosystem
Network analysis, a theory, a method, or both?
Network analysis → specific theory with specialized method?
Barry Wellman: „ ... broad intellectual approach, not only
narrow set of methods. ... Network Analysis implies
asymmetric world view“
→ Network analysis as “structural analysis”?
or
Network analysis → Toolbox for specific type of structural
analysis
- Sociometry
- Graph theory
- Blockmodelling
- Clusteranalyse
- Multidimensionale Skalierung (MDS)
Network analysis ≈ structural analysis?
There are many levels and types of structures in the social sciences:
• Super structures: cultural perceptions, beliefs, ideologies
• Institutional structures: social differenciation, norms, rule
systems
• Distributional structures: access, resources,
• Relational structures: exchange, communication, friendship, etc.
• Infrastructures: relations of production, technical infrastructures
Network Analysis is a methodological and conceptual toolbox for the
measurement, systematic description and analysis of relational
structures
Relational data vs. Attribute data and their representation
•
Attribute data: Properties of reserach objects → Variable analysis
Cases
Members
Staff
Budget
FoundYear
Org A
1000
10
1.000.000
1950
Org B
250
4
500.000
1972
Org C
300
2
100.000
1995
Relational data: Links between research objects → Network analysis
Representation by graphs and matrices
•
•
Influence
matrix
Organisations
Graph
B
Attribution of influence
A
Communication
C
Communication
matrix
A
B
C
A
0
1
1
B
0
0
C
1
0
A
B
C
A
0
1
1
0
B
1
0
1
2
C
1
1
0
(valued matrix)
(binary matrix)
One-mode network and two-mode networks
One-mode network
Two-mode network
Different classes (sets) of linked entities
Organisations
Events
A
1
B
2
Organisations
B
A
Communication
C
participation
C
2
Egocentric network
3 ego-centric networks: Relations focused and “around” Egos
Examples:
Support in families
Friendship
Sexual contacts and HIV
Innovation networks around firms
a1
a2
a3
A
a4
b1
d2
d1
B
b2
b3
d4
C
d3
d5
Representation of relational data: Influence relations
Graph
links
edges
B
points Matrix
nodes
C
A
Edge list
(e.g. relational databases)
A,B,1;
A,C,1;
C,A,1;
C,C,2;
121
131
311
332
Nodes are
numbered
A
B
C
A
0
1
1
B
0
0
0
C
1
0
2
Edge list
(for binary matrices)
1 23
3 13
C → C is binarized (2=1).
Types and Sources of Relational Data
Types
•
subjective vs. objective
•
episotic vs. recurrent
Sources
•
Surveys research → Questionnaires
•
Ethnographic research → Observations
•
Documentary research → Texts and documents
•
Statistical research → Statistical data bases
•
Experimental research → Experiments, simulations
Surveys and questionnaires
•
Row-based: each actor is asked about relations going to other actors
(e.g. to give advice).
•
Row and column based: each actor is asked not only whom they
give advice, but from whom they receive it.
•
•
If relations are not confirmed, need for coding criteria: Minimum,
maximum, average method.
Consensus method: each actor is asked to indicate the relationships
among every pair of actors
•
Since there are always many estimation (N = number of experts)
about existing relations, there is need for coding criteria.
Based on Borgatti (http://www.analytictech.com/networks/data.htm
Boundary specification: Delineation of networks & identification of actors
•
Complete/total vs. partial networks (Barnes)
• Sometimes natural boundaries exist
• But in the last instance, networks are global
•
Approaches for the specification of boundaries (Laumann/Knoke)
• Nominalist
• Positional
• Decisional
• Reputational
• Expert panels
• Event linkages
• Realist => Network Reputation
•
Name generators vs. lists of actors/organizations
Perceived relations measured by questionnaire
Binary relations. Example Chemicals Control Data
Influence reputation in a policy network: The case of German
chemicals control
Question: “Which organizations in this list had particular strong influence in
the policy-making process of the Chemicals Control Law?”
The entries had been stored and representation in a
binary square matrix
Perceived relations measured by questionnaire. 2-mode network
R1
A
R2
B
R3
Control of resources in the German
chemicals policy-making network
Question: “In order to influence political
decisions, organizations need
influence resources. On this card I
have listed some potential resources”
These informations produce a rectangle
matrix in which rows are organizations
and columns are resources
Org
Res1
Res2
Res3
A
0
1
0
B
1
1
1
Institutionalized access through committee memberships Two-mode
network
•
•
Q: In which of the following committees members of your
organizations had representative positions?
BUT: Documentary analysis is more reliable
• Result: 30 x 20-Matrix of committee memberships (C)
Committee memberships
BMA
BMLEF
BMI
..
IPU
1
1
1
..
BLAU
0
1
1
..
AGU
0
0
1
..
DFG.E
0
1
0
..
DFG.M
1
0
0
..
..
..
..
..
..
Committees
IPU
BMG.BIO
AGU
BLAU
DFG.ENV
DFG.MAK
AgA
BGA.AC
BMI.ST
IMA.CHEM
UBA.AG
BMT.AC
OECD.CP
EC.WG
ECCOR
GDC.OS
BGC.AC
AC.NS
CC.OS
EC.APP
Communication measured by a questionnaire.
Example: The „organizational state“ of Laumann and Knoke (1987:473)
Question
Would you please make a check in front of the name of all
organizations of this list with whom your organization regularly and
routinely discusses national energy policy matters!
List
OIL AND GAS INDUSTRY
Production Companies
___001. American Natural Resources Company
___002. Ashland Oil Inc
___003. Atlantic Richfield Co.
We do
___
___
___
Both
___
___
___
They Do
___
___
___
One-mode binary network: Sending & receiving scientific information
Question: a. “… From which organizations that are named on the
list your Organization (ORG) is receiving scientific information?”
b) “To which organization you are sending scientific
information?”
Valued relations by survey: Research on BIAs
Please mark those organisations, which from the perspective of
Your association are
• relevant (1) or
• especially significant (2)
to activities within Your sector.
1
1
2
3
…
ANGA - Verband privater Netzbetreiber - Satelliten- und Kabelkommunikation –
e.V.
BDZV - Bundesverband Deutscher Zeitungsverleger e.V.
2
BITKOM - Bundesverband Informationswirtschaft, Telekommunikation und neue
Medien e.V.
….
Continous observation in the Bank wiring observation room
•
•
Principle
• observer in room or area
records all interactions and
relations that exist or take place
over a continous periode in front
of the observer
Example
• Study done at the Western
Electric Company, Hawthorne
Works, Chicago (Roethlisberger/
Dickson)
• Records:
• Categorial: Quality, physical
examinations, mental tests
• Relational: Friendship,
conflicts, support, etc.
Bank wiring observation room
Helping and gaming relations
Observation of randomly chosen time intervals in ethnology
(time allocation method)
Principles
Time allocation method.
Observer shows up at random times and
records who is doing what to whom over
a short interval.
Goal is to have large number of these
snap-shots
Example
Hames, R. (1994) Ye'kwana Time
Allocation Data Base
Community in the Amazon: 18,000
records of 88 residents of the village of
Toki (1974-1976).
Reference: Borgerhoff Mulder, M.
Most individuals were recorded more
and T. Caro (1985). The use of
than 300 times at random hours of the quantitative observation techniques
day over a period of many months.
in anthropology. Current
Anthropology 26: 232-262.
Documents -Texts - Statistics
Principle
• Relations are coded on the basis of written or stored documents
• Statistical data bases
Examples
• Interlocking directorates (→ handbooks, cd-roms, ect. )
• Joint ventures, alliances among companies
• Diplomatic and military interaction among countries
• Migration flows
• Historical marriage records among 15th century italian families
• Intracampus email
Experiments
Principle
• Relations between research objects are triggered by measures of
experimental control
• Computer simulations
Examples
• Planting of rumors in schools or colleges and observed the spread
over time.
• Small World Experiments
• Stanley Milgram: Examination of how many links are required to
connect any two randomly chosen persons in the US.
S. Milgram. and J. Travers. ``An experimental study of the
small world problem.'' Sociometry, 32(4), pp. 425-443, 1969
The problem of random sampling
Can you use a sampling method to study complete
networks?
• Possible for multiple egocentric networks
• Very problematic for complete network, because of
very unequal distribution of network relations
Policy Network in Chemical Control
BEUC
ICE
BGC
CEF
BAM
EC
DIN
ILO
FHG
GDCH
DFG
OEKI
BUND
AGV
BBU
BMA
IGC
WHO
BEE
VDB
BAU
DAG
VAA
FDP
VCI
DNR
UBA
CDU
BGA
GSF
DH
SPD
DKFZ
BBA
BMFT
BMI
BMG
AGU
BT
BMW
Legend
0ECD
BML
UNEP
KFA
BR
SRU
IHT
Policy actors
Information
exchange
Centrality & influence within policy actor system
90
Centrality based on closeness information exchange
VCI
80
IGCPK
DFG
70
BMFT
UBA
BMI
BMA
BAU BGA
BR
Zero influence
repuation:
BMWI
EG
GDCH
AGV
GSF
60
BBA
0ECD
CDU
FDP
BUND
BAM
BT
FHG
SRU
DNR
DIN BBU
BGCHE
AGU
CEFIC SPD
DKFZ
UNEP
VAA
DIHT
KFA
WHO
ILO VDBWA
50 DH
ICEF
DAG
BMJFG
AGU
DH
DNR
DAG
DKFZ
KFA
FHG
DIN
BMELF
BEE
BEUC
OEKOI
UNEP
ILO
WHO
BEUC
BEE
ICEF
40
0,0
1,0
2,0
3,0
Normalized influence reputation
4,0
5,0