Abstract
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.