Key Takeaways

- SNA (Social Network Analysis) addresses relational and structural questions that traditional attribute-focused methods cannot answer, treating these as a distinct paradigm. Bibliometric evidence and the level of institutionalization confirm that SNA has matured into a fully recognized mainstream research methodology.
- SNA has built a robust infrastructure of dedicated journals, associations, textbooks, and software. With advances in ERGM, SAOM, and causal inference models, its methodological sophistication continues to grow, and the field has expanded into management, public health, education, policy, and beyond.
- Major international organizations including WHO and the World Bank have adopted SNA in practice, while challenges such as boundary specification, mixed-methods integration, privacy ethics, and causal inference misuse remain open areas for further development.

1. Introduction

Traditional social science and applied science research has long treated units of analysis as isolated entities, explaining social phenomena through the independent attributes individuals possess — a reductionist approach. But as modern society has grown exponentially more complex and digitalization has accelerated across all domains, phenomena that individual attributes alone cannot explain have moved to the center of research: the diffusion of knowledge, interdependence among actors, coordination and cooperation, and the dynamics of collective structures. Against this fundamental epistemological shift, Social Network Analysis (SNA) has established itself as a distinct and mainstream research methodology — one that goes beyond a simple analytical tool to mathematically and statistically examine relations, interactions, connections, positions, and structures.

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