Post on 16-Dec-2015
Quantitative Network Analysis: Perspectives on mapping change in
world system globalization
Douglas White
Robert Hanneman
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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 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)
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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?
• Can actors be classified according to which other kinds of actors they have ties to?
• Actors “roles” in the structure (e.g. “core nation”)
Regular equivalence of positions in the 13th century main European banking/trading network
Same scaling method as Smith and White 1992 that showed a virtually linear core-periphery structure in the contemporary world-trade system
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III. Dynamics
• Actors make relations
• Relations condition actors
• Micromacro links between probabilistic attachment bias and network topologies
• Macromicro 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
Proteome yeast alpha=2.4 (Amaral) hierarchical organization, reduces alpha
Greek Gods alpha=3.0 (H&J Newman) with no real organizational constraints, pure 'scale free' alpha (courtesy B. Walters)
Biotech alpha=2.0 (Powell, White, Koput, Owen-Smith) cohesive organization, reduces alpha
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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 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)
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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 macromicro feedback for quantitative changes and qualitative transformations of systemic properties at the level of local interaction