Social network analysis has its theoretical roots in the work of early sociologists such as Georg Simmel and Émile Durkheim, who wrote about the importance of studying patterns of relationships that connect social actors. Social scientists have used the concept of "social networks" since early in the 20th century to connote complex sets of relationships between members of social systems at all scales, from interpersonal to international. In the 1930s Jacob Moreno and Helen Jennings introduced basic analytical methods.[12] In 1954, John Arundel Barnes started using the term systematically to denote patterns of ties, encompassing concepts traditionally used by the public and those used by social scientists: bounded groups (e.g., tribes, families) and social categories (e.g., gender, ethnicity). Scholars such as Ronald Burt, Kathleen Carley, Mark Granovetter, David Krackhardt, Edward Laumann, Anatol Rapoport, Barry Wellman, Douglas R. White, and Harrison White expanded the use of systematic social network analysis.[13] Even in the study of literature, network analysis has been applied by Anheier, Gerhards and Romo,[14] Wouter De Nooy,[15] and Burgert Senekal.[16] Indeed, social network analysis has found applications in various academic disciplines, as well as practical applications such as countering money laundering and terrorism.

Hue (from red=0 to blue=max) indicates each node's betweenness centrality. Connections Homophily: The extent to which actors form ties with similar versus dissimilar others. Similarity can be defined by gender, race, age, occupation, educational achievement, status, values or any other salient characteristic.[17] Homophily is also referred to as assortativity.

Multiplexity: The number of content-forms contained in a tie.[18] For example, two people who are friends and also work together would have a multiplexity of 2.[19] Multiplexity has been associated with relationship strength.

Mutuality/Reciprocity: The extent to which two actors reciprocate each other's friendship or other interaction.[20]

Network Closure: A measure of the completeness of relational triads. An individual's assumption of network closure (i.e. that their friends are also friends) is called transitivity. Transitivity is an outcome of the individual or situational trait of Need for Cognitive Closure.[21]

Propinquity: The tendency for actors to have more ties with geographically close others.[20]

Distributions Bridge: An individual whose weak ties fill a structural hole, providing the only link between two individuals or clusters. It also includes the shortest route when a longer one is unfeasible due to a high risk of message distortion or delivery failure.[22]

Centrality: Centrality refers to a group of metrics that aim to quantify the "importance" or "influence" (in a variety of senses) of a particular node (or group) within a network.[23][24][25][26] Examples of common methods of measuring "centrality" include betweenness centrality,[27] closeness centrality, eigenvector centrality, alpha centrality, and degree centrality.[28]

Density: The proportion of direct ties in a network relative to the total number possible.[29][30]

Distance: The minimum number of ties required to connect two particular actors, as popularized by Stanley Milgram's small world experiment and the idea of 'six degrees of separation'.

Structural holes: The absence of ties between two parts of a network. Finding and exploiting a structural hole can give an entrepreneur a competitive advantage. This concept was developed by sociologist Ronald Burt, and is sometimes referred to as an alternate conception of social capital.

Tie Strength: Defined by the linear combination of time, emotional intensity, intimacy and reciprocity (i.e. mutuality).[22] Strong ties are associated with homophily, propinquity and transitivity, while weak ties are associated with bridges.