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