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 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)

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 (13 th 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 – R&D 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 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 13 th 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

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 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

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-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 13 th 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