Quantitative Network Analysis:
Perspectives on mapping change in
world system globalization
Douglas White
Robert Hanneman
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
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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
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I. Network structures in the world system
• Commodity chains
• Trade systems, transport and
communication
• Business networks
• City systems
• Interstate power
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Commodity chains
White’s analysis
of the inputoutput 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)
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Transportation and communication
• Volume, speed, cost of movement of:
• Bulk goods
• Luxury goods
• Information
• Between:
• Spatial locations
• Population centers
• Organizations/states
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Trade network (13th century)
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Business networks
•
•
•
•
•
Corporate interlocks
Market exchanges
Shared technology (e.g. licensing)
Shared niche space
Business groups
Evolution of the interorganization contracts
network in biotech – R&D and VC links for
1989 – 1999 (Powell, White, Koput and
Owen-Smith forthcoming, AJS)
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City systems
Settlement systems
have been seen as
systems that evolve
toward hierarchical
networks.
Networks like this may
have an exponential
degree distribution.
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Interstate power
•
•
•
•
Treaty/alliance networks
Exchange of recognition
Bloc membership
Co-membership in supra-national
organizations
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II. Summarizing structures
• Density, degree, reach
• Centrality and power
• Cohesion and sub-groups
• Positions and roles
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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?
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Centrality and power
• Which actors
have most
ties?
• Which actors
are closest to
most others?
• Which actors
are “between”
others?
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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)
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Roles and positions
• Can actors be
classified according to
which other actors they
have ties to?
Regular equivalence of positions in the 13th
century main European banking/trading
network
• 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
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virtually linear core-periphery structure in the contemporary worldtrade system
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
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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
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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
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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
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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
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Examples of scale-independent
networks and effects on alpha
Biotech alpha=2.0
(Powell, White,
Koput, OwenSmith) cohesive
organization,
reduces alpha
Proteome yeast
Greek Gods alpha=3.0
(H&J Newman) with
alpha=2.4 (Amaral)
no real organizational
hierarchical
constraints, pure
organization, reduces
'scale free' alpha
alpha
(courtesy B. Walters)
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Cohesion and shortcuts
• Competing tendencies toward closed
and cohesive local structures and…
• Extensive short-distance structures…
• Lead to “mixed” models, such as…
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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-cohesion 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
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Further applications of ring cohesion
• Nord-Pas-de-Calais study: spatial and kinconnected 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)
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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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