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Quantitative Network Analysis: Perspectives on mapping change in world system globalization

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Title: Quantitative Network Analysis: Perspectives on mapping change in world system globalization


1
Quantitative Network Analysis Perspectives on
mapping change in world system globalization
  • Douglas White
  • Robert Hanneman

2
The Social Network Approach
  • Structure as
  • Nodes and edges, or
  • Actors and relations
  • Dynamics as
  • Agency bottom up building of ties, but
  • Embedding within the emergent constraints of
    macro-structure

3
Structure
  • Nodes can be individuals, organizations,
    locations, or analytical aggregates
  • Relations can be material exchange, information
    flow, or shared status
  • What is fundamental are the ties or absence of
    ties between actors, in addition to the
    attributes of the actors

4
I. Network structures in the world system
  • Commodity chains
  • Trade systems, transport and communication
  • Business networks
  • City systems
  • Interstate power

5
Commodity chains
Whites analysis of the input-output matrix of
the Danish economy seen as a network scaled
by equivalence of position. (available for the
U.S., U.K, Holland, Italy, France, Australia)
6
Transportation and communication
  • Volume, speed, cost of movement of
  • Bulk goods
  • Luxury goods
  • Information
  • Between
  • Spatial locations
  • Population centers
  • Organizations/states

7
Trade network (13th century)
8
Business networks
  • Corporate interlocks
  • Market exchanges
  • Shared technology (e.g. licensing)
  • Shared niche space
  • Business groups

Evolution of the interorganization contracts
network in biotech RD and VC links for 1989
1999 (Powell, White, Koput and Owen-Smith
forthcoming, AJS)
9
City systems
Settlement systems have been seen as systems that
evolve toward hierarchical networks. Networks
like this may have an exponential degree
distribution.
10
Interstate power
  • Treaty/alliance networks
  • Exchange of recognition
  • Bloc membership
  • Co-membership in supra-national organizations

11
II. Summarizing structures
  • Density, degree, reach
  • Centrality and power
  • Cohesion and sub-groups
  • Positions and roles

12
Density, degree, reach
  • How much connection is there?
  • Which nodes have how much connection (social
    capital)?
  • Which actors are closest to, most influenced by
    which others?

13
Centrality and power
  • Which actors have most ties?
  • Which actors are closest to most others?
  • Which actors are between others?

14
Cohesion and sub-groups
  • Are there blocs or factions or sub-groups?
  • Which actors are connected, how tightly, to which
    groups?
  • What roles do actors have with respect to
    relations between groups?
  • Level of cohesive membership as a predictive
    variable

(Predictive Structural Cohesion theory)
15
Roles and positions
Regular equivalence of positions in the 13th
century main European banking/trading network
  • Can actors be classified according to which other
    actors they have ties to?
  • Can actors be classified according to which other
    kinds of actors they have ties to?
  • Actors roles in the structure (e.g. core
    nation)

Same scaling method as Smith and White 1992 that
showed a virtually linear core-periphery
structure in the contemporary world-trade system
16
III. Dynamics
  • Actors make relations
  • Relations condition actors
  • Micro?macro links between probabilistic
    attachment bias and network topologies
  • Macro?micro effects of network topologies on
    actor activities and behaviors

17
III. Network dynamics in the world system
  • How and why do world systems expand, contract,
    and change structure?
  • Homophily
  • Exchange
  • Power-laws (degree preference)
  • Cohesion and shortcuts

18
Homophily
  • Forming (or breaking) ties is not random
  • Actors may have preferences to form (or sustain)
    ties with similar others
  • The macro-result is local clustering and
    formation of factions

19
Network exchange
  • Ties may be formed (or dissolved) proportional to
    the cost/benefits to actors, and
  • Constraints due to presence of relations and
    existing embedding (alternatives available to
    each actor)
  • Macro-result may tend to structural holes and
    extended networks

20
Power laws
  • Actors with ties may use ties as social capital
    to accumulate further ties, and
  • Actors with few ties may prefer to establish ties
    with actors with more ties
  • Both tendencies have the macro-result of
    exponential distributions of ties
  • Exponential networks create relatively short
    average path-lengths (shortcuts) unless the hub
    distributions are too extreme

21
Examples of scale-independent networks and
effects on alpha
  • Proteome yeast alpha2.4 (Amaral) hierarchical
    organization, reduces alpha

Greek Gods alpha3.0 (HJ Newman) with no real
organizational constraints, pure 'scale free'
alpha (courtesy B. Walters)
Biotech alpha2.0 (Powell, White, Koput,
Owen-Smith) cohesive organization, reduces alpha
22
Cohesion and shortcuts
  • Competing tendencies toward closed and cohesive
    local structures and
  • Extensive short-distance structures
  • Lead to mixed models, such as

23
Ring Cohesion
  • Cohesion is an important predictor of network
    attachment, demonstrated in schools (AdHealth),
    industry (e.g. biotech), kinship, social class,
    and other fields and organizations. Ring cohesion
    theory focuses on preferential attachment-to-cohes
    ion mechanisms and how they are constructed.
  • Ring cohesion analysis has now been completed for
    biotech and numerous kinship examples (work
    underway with Wehbe, Houseman) and is being done
    on the 13th C. world-system networks

24
Further applications of ring cohesion
  • Nord-Pas-de-Calais study spatial and
    kin-connected dimensions of ring cohesion (joint
    scaling model with Hervé Le Bras)
  • Networks of the previous world-system (13th
    century trade and monetary linkages with Peter
    Spufford)
  • Networks of the first world-system (Jemdet Nasr
    Henry Wright)

25
IV. Conclusions
  • How networks are formed (probabilistic biases),
    how multiple networks and levels interlock, what
    is transmitted has powerful predictions,
  • Including micro-macro (predictive linkages) with
    more global structural and dynamical properties
    of networks and their structural transformations
  • With macro?micro feedback for quantitative
    changes and qualitative transformations of
    systemic properties at the level of local
    interaction
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