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Linking Social Capital to Small-Worlds: A Look at Local and Network-Level Processes and Structure

In the past decade, two topics have generated much interest in the idea of social networks and network analysis. These are social capital, popularised by Robert Putnam, and small-worlds, popularised by Duncan Watts and Albert-László Barabási. Social capital highlights local processes and network structures, theorizing the ways in which relations and their patterns link individuals and groups to resources and beneficial outcomes. Small-worlds emphasizes global network structures, describing how large, heterogeneous networks can nonetheless appear small to individual actors, largely as a result of the high clustering and weak, bridging ties that make up these networks' structure. Although social capital and small-worlds share social networks as a common basis, they emphasize different sides of a spectrum: social capital focuses on the local and small-worlds on the global. In addition, both focus on seemingly different social phenomena: social capital emphasizes access to resources, whereas small-worlds emphasize the tension of actors living in a social world that is simultaneously large and small. In spite of these differences, the literature points towards overlaps in the ways in which network structure is described: both social capital and small-worlds discuss structures of openness and closure, and these structural overlaps provide a means by which to start exploring, on a theoretical level, additional ways in which to bring about a synthesis of the two bodies of literature. In this paper, I situate social capital as an explanatory framework for the emergence of small-worlds. I do this through three phases: first, I discuss how each topic describes and theorizes opennenss and closure. Next, I develop a series of propositions that show how social capital can be linked to small-worlds in a coherent framework. Finally, I offer an empirical illustration of these propositions through the use of p*, one of the models from the larger family of exponential random graph models (ERGMs), which allow analysts to test the probability of certain local structural tendencies in a given network.