SNA

Social network analysis (SNA) is a set of methods for analyzing social structures through networks of actors connected by relationships. In SNA, entities such as people, organizations, or countries are represented as nodes, while ties representing interactions, flows, or affiliations connect these nodes. The approach emphasizes patterns of connections and how they influence behavior, information flow, and outcomes.

SNA has roots in sociology and anthropology and has grown into a broader network science field. Early work focused on mapping social circles and visualizing connections, while later research developed quantitative measures of network structure. Core concepts include centrality (a node’s importance within the network), density (the proportion of possible ties that are present), and path metrics such as distance and reach. Other important ideas include structural holes, community structure, multiplex networks (ties of different kinds), and network motifs.

Practices in SNA combine data collection, computation, and interpretation. Data can come from surveys, organizational records, or digital traces. Analyses rely on specialized software and statistical models, such as centrality calculations, cluster detection, Exponential Random Graph Models (ERGMs), and stochastic actor-oriented models for longitudinal networks. Visualization tools help reveal patterns and clusters.

Applications span many domains, including organizational analysis (informal communication and collaboration), epidemiology (spread of disease), innovation and knowledge flows, criminal networks, and online social platforms. Ethical considerations are central, given privacy concerns and the potential biases and incompleteness of network data. Limitations include data quality, sampling bias, and the challenge of distinguishing correlation from causal influence in networked settings. See also centrality measures and network theory.