A Social Network Analysis (SNA) perspective can be employed to understand political, economic and social phenomena, in particular:

• interactions between individual actors, groups or organisations;

• resource and information flows;

• social influence.

SNA provides insight into structural constraints of actors dependent not only on their own relations but also on the way other actors are related. Embedding actors within the set of their interactions allows for a better understanding of the distribution of power, the effective impact of social and political action, the influence of cultural and social factors on individual behaviour. 

All social interaction can be viewed in network terms. This is because networks account for the relations between actors. SNA assists in examining individuals within their institutional context and relational contacts. Indeed some sociologists examine all interactions that relate to individuals, social groups or society as dependent on their networks.

The approach comprises a paradigm in as far as it is based on mathematical foundations of graph theory and the insights allowed by relational theories of social interaction. It is also a methodology that can be applied across scientific disciplines and has a well developed set of predominantly descriptive statistics. Most widely used are measures of centrality, density, transitivity, reciprocity and brokerage. There are indicators to examine the way groups cohere, fraction or cluster. Several algorithms are available for the study of network properties. 

Network analysis requires a special set of statistical tools for constructing tests of significance. This is because as a method it violates an elementary principle of statistical inference, namely that of independent observations. The use of simulation techniques either through ‘bootstrapping’ or through Monte Carlo Markov Chains often provides the background to a rigorous hypothesis testing of network properties.

SNA can be employed along other methodological tools in social science: it easily complements more traditional qualitative and quantitative techniques and is conducive to method triangulation and trans-disciplinary research.