Social network analysis methods for international development

Social networks are “a set of players and patterns of exchange of information and/or goods among these players.”2 The intellectual home of network analysis is in sociology, where Durkheim established the study of social relationship patterns, and Granovetter advanced the importance of “weak ties” (distant acquaintances) in relational phenomena, such as successfully acquiring leads for job opportunities.3 Later, in political science, Putnam explained waning social capital in United States by the breakdown of community networks, such as bowling leagues and economic structures like labor unions.4 Only recently, though, have researchers drawn on the methods and empirical basis of SNA to elevate it to a mainstream analytic tool. The rapid growth in published papers and grant funding over the past 15 years demonstrates this uptake.5

SNA is a quantitative analysis tool used to identify and understand relationships between people or, in other words, social networks. It visually displays data so researchers can see behavioral relationships at the micro level (individual, institutional) and patterns at the macro or network level. SNA has the flexibility to treat networks as both independent and dependent variables. For example, it can help answer how differences in individuals’ networks (independent variable) explain their risk for contracting COVID-196 or how racially segregated schools affect a young person’s friendship networks (dependent variable).7

The data used in SNA can include secondary sources, such as social media data (connections, likes, shares, etc.); evidence of collaboration, such as co-authoring a paper; or administrative records such as school attendance, employment history, or club membership. Surveys can also collect primary data for SNA, with respondents asked to answer questions about their relationships, exchanges, and affiliations. Surveys often ask about the level of respondents’ connection to the others (i.e., frequency of communication); the nature of those exchanges (information, goods and services, collaboration); and the value the respondent assigns to them.

SNA data are typically analyzed and interpreted in two ways. First, a set of network metrics can characterize the network and quantify its dimensions. Typical network measures include density, reciprocity, transitivity, centralization, and modularity. See Figure 1 for definitions and explanations of these metrics, among others. Second, researchers can also explore and interpret social networks visually. Various software tools map the connections among network actors and produce social network graphs or “sociograms.” In these graphs, colors demark different kinds of actors, or nodes on the graph. The sizes of the nodes indicate the levels of connectedness. The position and partitioning of nodes in the network maps visualizes the network structure, including central actors, isolated actors, bridging actors, and any sub-groupings or cliques. See the Annex for more on the computing software needed and approaches to network visualization.

Figure 1

Common social network analysis measures

SNA metrics are often grouped into two categories to characterize networks by their level of (1) network closure or cohesion, measured by levels of density, reciprocity, transitivity, degree centrality, and shortest path, and (2) network heterogeneity, measured by modularity. Networks with higher levels of closure are associated with higher familiarity, trust, and social capital, and more-efficient exchange of information, goods, or services. More-heterogeneous networks are considered more effective at mobilizing resources, given that network exchanges often require coordination of many skills and various inputs across different types of actors.

For SNA to be useful or effective, it should be methodologically well-aligned to answer clear research questions. In international development, it presents a potentially useful tool for understanding a range of network relationships, including levels of collaboration and exchange; the existence of central actors; excluded populations; and absent connections among individuals, organizations, or groups. Two recent applications of SNA to international development used SNA to understand collaboration among different types of actors that programs had been unable to assess adequately in the past.