Social Network Analysis
Social Network Analysis (SNA) · Also known as: SNA, network analysis, sociometric analysis, relational analysis
Social Network Analysis (SNA) is a structural method that maps and measures relationships and flows between people, groups, organizations, or other entities modeled as nodes connected by ties (edges). Rather than focusing on individual attributes, SNA reveals how the pattern of connections shapes behavior, influence, information flow, and outcomes within a system.
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When to use it
Use SNA when the research question is fundamentally relational — about how connections, flows, or structural positions shape outcomes — and when relational data (ties between actors) can be collected or reconstructed. It is well-suited to studying information diffusion, collaboration patterns, influence hierarchies, organizational structures, citation networks, and social cohesion. Do not use SNA when you have only attribute data (no tie data), when the network boundary cannot be reasonably defined, when the sample is too sparse to yield meaningful metrics, or when the research question is about individual-level attributes rather than relational structure.
Strengths & limitations
- Captures structural phenomena — brokerage, cohesion, centrality — invisible to attribute-based methods.
- Applicable to diverse domains: social, organizational, bibliometric, biological, and digital networks.
- Rich suite of metrics and models allows both descriptive and inferential analysis.
- Integrates naturally with visualization tools that communicate complex structure intuitively.
- Applicable to both small (survey-based) and very large (computational) networks.
- Compatible with longitudinal and multilayer extensions for dynamic or multiplex networks.
- Network boundary specification is consequential and often theoretically ambiguous.
- Relational data collection is burdensome; survey-based name generators suffer recall bias and non-response.
- Many SNA metrics assume complete network data; missing ties distort centrality estimates.
- Standard SNA metrics treat nodes as independent, violating the independence assumption required by classical statistical tests.
- Large networks require computational resources and specialized software (e.g., igraph, NetworkX, Gephi).
Frequently asked
What is the minimum network size for meaningful SNA?
There is no strict minimum, but very small networks (fewer than ~15 nodes) yield unstable centrality rankings sensitive to single tie changes. Descriptive SNA can work on small networks; inferential methods like ERGM require larger networks (typically 30+ nodes) for stable estimates.
Can I use SNA with incomplete or sampled network data?
Missing ties are a serious threat to validity. Strategies include sensitivity analysis, link-prediction imputation, or ego-network designs that do not require full network data. Report the extent of missingness and test robustness of key conclusions.
How do I choose between different centrality measures?
The choice should be theoretically motivated. Degree centrality captures direct activity or exposure; betweenness centrality captures brokerage and control over flows; closeness centrality captures speed of reaching others; eigenvector centrality captures influence through high-status connections. Each answers a different structural question.
How do I test hypotheses statistically in SNA?
Standard regression violates the independence assumption. Use permutation-based methods (QAP correlation and regression) for tie-level hypotheses, or Exponential Random Graph Models (ERGMs) for network-level structural hypotheses. For longitudinal data, Stochastic Actor-Based Models (SAOMs) are appropriate.
Is visualization the same as analysis?
No. Network visualization is a communication tool; node placement in force-directed algorithms is determined by aesthetic criteria, not by structural importance. Always report quantitative metrics alongside visualizations.
Sources
- Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1
- Scott, J. (2017). Social Network Analysis (4th ed.). SAGE Publications. ISBN: 978-1-4739-5515-1
How to cite this page
ScholarGate. (2026, June 3). Social Network Analysis (SNA). ScholarGate. https://scholargate.app/en/network-analysis/social-network-analysis
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- Betweenness CentralityNetwork analysis↔ compare
- Closeness CentralityNetwork analysis↔ compare
- Degree CentralityNetwork analysis↔ compare
- Eigenvector CentralityNetwork analysis↔ compare
- Modularity AnalysisNetwork analysis↔ compare